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Apple_ProRes_White_Paper

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Apple ProRes White PaperOctober 2012Contents3 Introduction4 Apple ProRes Family Overview6 Properties of Digital ImagesFrame Size (Full-Width Versus Partial-Width)Chroma SamplingSample Bit Depth9 Properties of Apple ProRes CodecsData RateQualityPerformanceApple ProRes 4444 Alpha Channel Support 18 AppendixTarget Data Rates20 GlossaryIntroductionApple ProRes is one of the most popular and commonly used codecs in professional post-production. The Apple ProRes family of video codecs has made it both possible and affordable to edit full-frame, 10-bit, 4:2:2 high-definition (HD), 2K, 4K, and 5K video sources with multistream performance in Final Cut Pro X. This white paper provides in-depth information about all five members of the Apple ProRes family, including technical specifications and performance metrics.All members of the Apple ProRes family provide an unparalleled combination ofmultistream, real-time editing performance coupled with impressive image quality at reduced storage rates. Additionally, all five codecs preserve standard-definition (SD), HD, 2K, 4K, and 5K frame sizes at full resolution.As a variable bit rate (VBR) codec technology, Apple ProRes uses fewer bits on simple frames that would not benefit from encoding at a higher data rate. All Apple ProRes codecs are frame-independent (or “intra-frame”) codecs, meaning that each frame is encoded and decoded independently of any other frame. This technique provides the greatest editing performance and flexibility.• Apple ProRes 4444: The Apple ProRes 4444 codec preserves motion image sequences originating in either 4:4:4 RGB or 4:4:4 Y’C B C R color spaces. With a remarkably low data rate (as compared to uncompressed 4:4:4 HD), Apple ProRes 4444 supports 12-bit pixel depth with an optional, mathematically lossless alpha channel for true 4:4:4:4 support. Apple ProRes 4444 preserves visual quality at the same high level as Apple ProRes 422 (HQ), but for 4:4:4 image sources, which can carry the highest possible color detail.Apple ProRes 4444 is ideal for next-generation video and the most pristine computer graphics sources for cinema-quality compositing. For projects that previously required the Animation codec, Apple ProRes 4444 is a modern replacement with real-timeplayback support in Final Cut Pro. Because color detail is preserved, Apple ProRes 4444 is ideal for color grading with 4:4:4 image sources.• Apple ProRes 422 (HQ): Apple ProRes 422 (HQ) offers visually lossless preservation of the highest-quality professional HD video that a (single-link) HD-SDI signal can carry. For this reason, Apple ProRes 422 (HQ) has enjoyed widespread adoption across the video post-production industry. This codec supports full-width, 4:2:2 video sources at 10-bit pixel depths, while retaining its visually lossless characteristic through many generations of decoding and reencoding.Apple ProRes 422 (HQ) can be used both as an intermediate codec to accelerate workflows for complex, compressed video sources and as an affordable,high-performance alternative to uncompressed 4:2:2 video.Apple ProRes Family Overview• Apple ProRes 422: Apple ProRes 422 offers nearly all the benefits of its big brother, Apple ProRes 422 (HQ), but at a significantly lower data rate. It provides visually lossless coding performance for the same full-width, 10-bit, 4:2:2 sequences as Apple ProRes 422 (HQ) with even better multistream real-time editing performance.• Apple ProRes 422 (LT): Like Apple ProRes 422 (HQ) and Apple ProRes 422, theApple ProRes 422 (LT) codec supports full-width, 10-bit video sequences, but at a target data rate even lower than that of its siblings. Apple ProRes 422 (LT) weighs inat 100 Mbps or less, depending on the particular video format. Apple ProRes 422 (LT) balances incredible image quality with small file sizes, and is perfect for digital broadcast environments where storage capacity and bandwidth are often at a premium.Apple ProRes 422 (LT) is ideal for live multicamera and on-location productions where large amounts of footage are acquired to disk.• Apple ProRes 422 (Proxy): The lowest-bandwidth member of the Apple ProRes family is Apple ProRes 422 (Proxy). This codec maintains HD data rates below 36 Mbps, yet like its higher-rate Apple ProRes 422 siblings, it supports full-frame, 10-bit, 4:2:2 video. Apple ProRes 422 (Proxy) is intended for draft-mode or preview uses where low data rates are required, yet full-resolution video is desired. It is the ideal format for use in offline editing workflows on notebook computers or other systems where storage is at a premium and you have a large quantity of source media.Today’s HD workflows demand offline video formats that support native frame size and aspect ratio. Apple ProRes 422 (Proxy) supports full 1920 x 1080 and 1280 x 720 resolutions, enabling full HD resolution while editing, and accurate representation of Final Cut Pro motion effects from the creative stages through finishing.Properties of Digital Images The technical properties of digital images correspond to different aspects of imagequality. For example, high-resolution HD images can carry more detail than their lower-resolution SD counterparts. 10-bit images can carry finer gradations of color,thereby avoiding the banding artifacts that can occur in 8-bit images.The role of a codec is to preserve image quality as much as possible at a particular reduced data rate, while delivering the fastest encoding and decoding speed.The Apple ProRes family supports the three key properties of digital images that contribute to image quality—frame size, chroma sampling, and sample bit depth—while offering industry-leading performance and quality at each supported data rate. In order to appreciate the benefits of the Apple ProRes family as a whole andto choose which family members to use in various post-production workflows, it is important to understand these three properties.Frame Size (Full-Width Versus Partial-Width)Many video camcorders encode and store video frames at less than the full HD widths of 1920 pixels or 1280 pixels, for 1080-line or 720-line HD formats, respectively. When such formats are displayed, they are upsampled horizontally to full HD widths, but they cannot carry the amount of detail possible with full-width HD formats.All Apple ProRes family members can encode full-width HD video sources (sometimes called “full-raster” video sources) to preserve the maximum possible detail an HD signal can carry. Apple ProRes codecs can also encode partial-width HD sources if desired, thereby averting potential quality and performance degradation that results from upscaling partial-width formats prior to encoding.Chroma SamplingColor images require three channels of information. In computer graphics, a pixel’s color is typically defined by R, G, and B values. In traditional digital video, a pixel is represented by Y’, C B , and C R values, where Y’ is the “luma” or grayscale value and C B and C R contain the “chroma” or color-difference information. Because the eye isless sensitive to fine chroma detail, it is possible to average together and encode fewer C B and C R samples with little visible quality loss for casual viewing. This technique, known as chroma subsampling , has been used widely to reduce the data rate of video signals. However, excessive chroma subsampling can degrade quality for compositing, color correction, and other image-processing operations. The Apple ProRes family handles today’s popular chroma formats as follows:• 4:4:4 is the highest-quality format for preserving chroma detail. In 4:4:4 image sources, there is no subsampling, or averaging, of chroma information. There are three unique samples, either Y’, C B , and C R or R, G, and B, for every pixel location. Apple ProRes 4444 fully supports 4:4:4 image sources, from either RGB or Y’C B C R color spaces. The fourth “4” means that Apple ProRes 4444 can also carry a unique alpha-channel sample for every pixel location. Apple ProRes 4444 is intended to support 4:4:4:4 RGB+Alpha sources exported from computer graphics applications such as Motion, as well as 4:4:4 video sources from high-end devices such as dual-link HDCAM-SR.• 4:2:2 is considered a high-quality, professional video format in which the chroma values of Y’C B C R images are averaged together such that there is one C B and one C R sample, or one “C B /C R chroma pair,” for each Y’ (luma) sample. This minimal chroma subsampling has traditionally been considered adequate for high-quality compositing and color correction, although better results can be achieved using 4:4:4 sources. 4:2:2 sources are generated by many popular higher-end video camcorder formats, including DVCPRO HD, AVC-Intra/100, and XDCAM HD422/50. All Apple ProRes 422 family members fully support the chroma resolution inherent in 4:2:2 video formats.• 4:2:0 and 4:1:1 have the least chroma resolution of the formats mentioned here, with just one C B /C R chroma pair for every four luma samples. These formats are used in a variety of consumer and professional video camcorders. Depending on the quality of a camera’s imaging system, 4:2:0 and 4:1:1 formats can provide excellent viewing quality. However, in compositing workflows it can be difficult to avoid visible artifacts around the edges of a composited element. HD 4:2:0 formats include HDV, XDCAM HD, and AVC-Intra/50. 4:1:1 is used in DV. All Apple ProRes 422 formats can support 4:2:0 or 4:1:1 sources if the chroma is upsampled to 4:2:2 prior to encoding.4:2:24:4:44:2:0 (interstitial siting)4:1:1••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••Image pixel •Chroma sampleSample Bit DepthThe number of bits used to represent each Y’, C B, or C R (or R, G, or B) image sample determines the number of possible colors that can be represented at each pixel location. Sample bit depth also determines the smoothness of subtle color shading that can be represented across an image gradient, such as a sunset sky, without visible quantization or “banding” artifacts.Traditionally, digital images have been limited to 8-bit samples. In recent years the number of professional devices and acquisition techniques supporting 10-bit and even 12-bit image samples has increased. 10-bit imagery is now often found in 4:2:2 video sources with professional digital (SDI, HD-SDI or even HDMI) outputs. 4:2:2 video sources rarely exceed 10 bits, but a growing number of 4:4:4 image sources claim12-bit resolution, though with sensor-derived images the least significant one or two bits may have more noise than signal. 4:4:4 sources include high-end film scanners and film-like digital cameras and can include high-end computer graphics.Apple ProRes 4444 supports image sources up to 12 bits and preserves alpha sample depths up to 16 bits. All Apple ProRes 422 codecs support up to 10-bit image sources, though the best 10-bit quality will be obtained with the higher bit rate family members—Apple ProRes 422 and Apple ProRes 422 (HQ). (Note: Like Apple ProRes 4444, all Apple ProRes 422 codecs can in fact accept image samples even greater than 10 bits, although such high bit depths are rarely found among 4:2:2 or 4:2:0 video sources.)Every image or video codec can be characterized by how well it behaves in three critical dimensions: compression, quality, and complexity. Compression means data reduction, or how many bits are required compared to the original image. For image sequences or video streams, compression means data rate, expressed in bits/sec for transmission or bytes/hour for storage. Quality describes how closely a compressed image resembles the original. “Fidelity” would therefore be a more accurate term, but “quality” is the term widely used. Complexity relates to how many arithmetic operations must be computed to compress or decompress an image frame orsequence. For software codec implementations, the lower the complexity, the greater the number of video streams that can be decoded simultaneously in real time, resulting in higher performance within post-production applications.Every image or video codec design must make a tradeoff among these threeproperties. Because codecs used within professional camcorders or for professional video editing must maintain high visual quality, the tradeoff amounts to one of data rate versus performance. For example, AVCHD camcorders can produce H.264 video streams with excellent image quality at low data rates. However, the complexity of the H.264 codec is very high, resulting in lower performance for real-time video editing with multiple video streams and effects. In comparison, Apple ProRes features excellent image quality as well as low complexity, which results in better performance for real-time video editing.The following sections describe how the various Apple ProRes codecs behave and compare to one another in terms of these three important codec properties: data rate, quality, and performance .Data RateThe Apple ProRes family spans a broad range of data rates to support a variety of workflow and application purposes. This section describes how Apple ProRes data rates compare to each other and to uncompressed video. The section also illustrates how frame size and frame rate affect Apple ProRes data rates. Finally, the text includes information on the variable bit rate (VBR) nature of the Apple ProRes codec family.Properties of Apple ProRes CodecsThe bar chart below shows how the data rate of each Apple ProRes format compares to uncompressed, full-width (1920 x 1080), 4:4:4 12-bit and 4:2:2 10-bit imagesequences at 29.97 frames/sec. The chart shows that even the two highest-quality Apple ProRes formats—Apple ProRes 4444 and Apple ProRes 422 (HQ)—offer significantly lower data rates than their uncompressed counterparts.7501,5002,2503,000Uncompressed 12-bit 4:4:4ProRes 4444[no alpha]Uncompressed 10-bit 4:2:2ProRes 422 (HQ)ProRes 422ProRes 422 (LT)ProRes 422 (Proxy)Data Rates - Uncompressed and Apple ProRes at 1920 x 1080, 29.97 fpsM b /s The data rates shown in the bar chart above are for “full-width” (1920 x 1080) HD frames at 29.97 frames/sec. The Apple ProRes family also supports the 720p HD format at its full width (1280 x 720). In addition to full-width HD formats, Apple ProRes codecs support three different “partial-width” HD video formats used as the recording resolutions in a number of popular HD camcorders: 1280 x 1080, 1440 x 1080, and 960 x 720.The data rate of an Apple ProRes format is determined primarily by three key factors: Apple ProRes codec type, encoded frame size, and frame rate. The chart below shows some examples of how varying any one of these factors changes an Apple ProRes format’s data rate. A table of data rates for a number of Apple ProRes formats supported for real-time editing in Final Cut Pro can be found in the appendix.23.976 fps29.97 fps03875113150Data Rates - Apple ProRes 422 (LT) versus Apple ProRes 422Mb/sApple ProRes is a variable bit rate (VBR) video codec. This means that the number of bits used to encode each frame within a stream is not constant, but varies from one frame to the next. For a given video frame size and a given Apple ProRescodec type, the Apple ProRes encoder aims to achieve a “target” number of bits per frame. Multiplying this number by the frames per second of the video format being encoded results in the target data rate for a specific Apple ProRes format.Although Apple ProRes is a VBR codec, the variability is usually small. The actual data rate is usually close to the target data rate. For a given Apple ProRes format, there is also a maximum number of bits per frame that is never exceeded. This maximum is approximately 10 percent more than the target number of bits per frame. The graph below plots the actual number of bits used per frame in an example Apple ProRes video sequence.Compressed Frame Sizes - Apple ProRes 422200040006000800010000Frame0400000800000F r a m e S i z e (B y t e s )MaxTargetSequence depicted is ASC/DCI Standard Evaluation Material (StEM) Mini-Movie at 1920 x 1080.Note that for this particular sequence of over 10,000 frames, only one frame uses the maximum number of bits and most frames are clustered within a few percent of the target. However, many frames use significantly fewer bits than the target. This is because Apple ProRes encoders only add bits to a frame if doing so will produce a better match to the original image. Beyond a certain point, simple image frames, such as an all-black frame with a few words of text, incur no quality benefit if more bits are added. Apple ProRes encoders do not waste bits on any frame if adding more will not improve the fidelity.QualityWhile the ability to produce high-quality output is a key attribute of image and video codecs, it is quality preservation—or fidelity—that is the actual goal of a codec. Imagery often goes through many stages of processing prior to Apple ProRes encoding, and these stages may add visible flaws, or “artifacts,” to the images. If an image sequence has visible artifacts to begin with, Apple ProRes will perfectly preserve these artifacts, which can make viewers mistakenly think such flaws are caused by the Apple ProRes codec itself. The goal of every Apple ProRes family member is to perfectly preserve the quality of the original image source, be it good or bad.The quality-preserving capability of the various Apple ProRes codecs can beexpressed in both quantitative and qualitative terms. In the field of image and video compression, the most widely used quantitative measure of image fidelity is peak signal-to-noise ratio (PSNR). PSNR is a measure of how closely a compressed image (after being decompressed) matches the original image handed to the encoder. The higher the PSNR value, the more closely the encoded image matches the original. The graph below plots the PSNR value for each image frame in a test sequence for three different codecs: Apple ProRes 422 (HQ), Avid DNxHD, and Panasonic D5.PSNR Comparison - Apple ProRes, DNxHD, and D5200040006000800010000Frame4050607080L u m a P S N R (d B )Measured using ASC/DCI Standard Evaluation Material (StEM) Mini-Movie at 1920 x 1080.The next graph shows the same sequence plotted for each Apple ProRes 422 codec. As the graph shows, there is a difference in PSNR between one family member and the next. These differences correspond to the comparative data rates of the Apple ProRes 422 codecs. PSNR for Apple ProRes 422 (HQ) is 15-20 dB higher than that for Apple ProRes 422 (Proxy), but the Apple ProRes 422 (HQ) stream has nearly five times the data rate of the Apple ProRes 422 (Proxy) stream. The benefit of higher fidelity comes at the cost of larger file sizes, so it is important to select the0200040006000800010000Frame20406080L u m a P S N R (d B )Measured using ASC/DCI Standard Evaluation Material (StEM) Mini-Movie at 1920 x 1080.In addition to indicating visual fidelity, the difference in PSNR values also denotes headroom. For example, if you were to view the original sequence used in the graph above, and then view the Apple ProRes 422 (HQ) and Apple ProRes 422 encoded versions of the same stream, all three would look visually identical. However, thehigher PSNR value for Apple ProRes 422 (HQ) indicates greater quality headroom. This increased headroom means that an image sequence can be decoded and reencoded over multiple generations and still look visually identical to the original, as shown in the graph below:203040506013579Multigeneration PSNRL u m a P S N R (d B )GenerationsBecause PSNR is not a perfect measure of compressed image fidelity—there is no particular PSNR number that can absolutely guarantee that a compressed image will have no visible difference from the original—it is useful to have some qualitative description of expected image quality for each Apple ProRes codec type. Note that in the table below, the qualitative description for Apple ProRes 4444 (without an alpha channel) is identical to that for Apple ProRes 422 (HQ). This is because Apple ProRes 4444, though its target bit rate is 50 percent higher than that of Apple ProRes 422 (HQ), uses extra bits to encode the greater number of chroma samples in 4:4:4 at the same high quality headroom ensured by Apple ProRes 422 (HQ) for 4:2:2 sources.Apple ProRes Codec Visible differences (1st gen.) Quality headroomProRes 4444 Virtually never Very high, excellent for multi-gen. finishing ProRes 422 (HQ) Virtually never Very high, excellent for multi-gen. finishing ProRes 422 Very rare High, very good for most multi-gen. workflows ProRes 422 (LT) RareGood for some multi-gen. workflowsProRes 422 (Proxy)Subtle for high-detail imagesOK, intended for first-gen. viewing and editingPerformanceThe Apple ProRes family of codecs is designed for speed, and high speed of both encoding and decoding is essential to avoid workflow bottlenecks.Fast decoding is especially critical for multistream, real-time editing in Final Cut Pro. The Apple ProRes codec family performs exceptionally well in this regard. For each Apple ProRes codec type, the following chart shows the number of full-width, 1920 x 1080 HD streams that can be edited simultaneously in real time on aMacBook Pro computer. In practice of course, you may not often need to edit five, six, or more streams simultaneously, but this chart gives an idea of how much processing time will be available for real-time titling, effects, and so on, when just one, two, or three streams are being used.ProRes 422 (Proxy)*ProRes 422 (LT)*ProRes 422ProRes 422 (HQ)481216MacBook Pro – Final Cut Pro Multicam Streams at 1920 x 1080, 23.976 fpsNumber of simultaneous streams*The Final Cut Pro X multicam feature allows you to view up to a maximum of 16 angles simultaneously while switching or cutting angles in real time.Testing conducted by Apple in October 2012 using OS X Mountain Lion v10.8.2 and shipping 2.6GHz quad-core Intel Core i7-based 15-inch MacBook Pro with Retina display units configured with 512GB of flash storage and 8GB of RAM. Testing conducted with a prerelease version of Final Cut Pro X using 7-minute, 30-second 1920 x 1080p24 ASC-DCI Standard Evaluation Material multicam clips for each content type. MacBook Pro continuously monitors system thermal and power conditions, and may adjust processor speed as neededto maintain optimal system operation. Performance may vary depending on system configuration and content.ProRes 4444(w/o alpha)Today’s Mac notebook and desktop machines rely on multicore processing, so the speed of a fast editing decoder must scale up—meaning that decoding time per frame should decrease—as the number of processing cores increases. Many industry codec implementations “hit the wall” and do not realize further performance gains as more processors are added, but Apple ProRes codecs continue to get faster as more cores are added, as the following chart shows.1242101051520Multiprocessor Scaling – Apple ProRes 422 (HQ) at 1920 x 1080Decoding time (ms per frame; shorter is faster)N u m b e r o f p r o c e s s o r c o r e sTesting conducted by Apple in October 2012 using OS X Mountain Lion v10.8.2, a prerelease version ofFinal Cut Pro X (10.0.6), and shipping Mac Pro 3.06GHz 12-core Intel Xeon units configured with 12GB of RAM.Performance may vary depending on system configuration, content, and performance measurement tool use.6Apple ProRes decoders are designed to work especially well as high-quality,high-performance editing codecs for Final Cut Pro. Not only are they fast for decoding video at full frame size and quality, but they are even faster at decoding frames at “half-size” frame (1/2 height and 1/2 width). Especially for high-resolution formats like HD and 2K, half-size images provide plenty of onscreen detail for making editing decisions.The chart below shows that half-size decoding is substantially faster than already-fast full-size decoding, especially for the higher-quality Apple ProRes codecs. The faster decoding speed means more CPU time is available for decoding more streams or more real-time effects.ProRes 422 (Proxy)ProRes 422 (LT)ProRes 422ProRes 422 (HQ)ProRes 4444(w/o alpha)246Reduced Resolution Decoding Speed at 1920 x 1080Decoding time (ms per frame; shorter is faster)Testing conducted by Apple in October 2012 using OS X Mountain Lion v10.8.2 and shipping 2.6GHzquad-core Intel Core i7-based 15-inch MacBook Pro with Retina display units configured with 512GB of flash storage and 16GB of RAM. MacBook Pro continuously monitors system thermal and power conditions, and may adjust processor speed as needed to maintain optimal system operation. Performance may vary depending on system configuration, content, and performance measurement tool use.Although fast decoding speed is the primary factor in real-time editing performance, fast encoding speed is also important for key steps in post-production workflows. Like Apple ProRes decoders, the Apple ProRes family of encoders have all been built as efficient software implementations, and fast encoding is achieved through efficient use of multicore processors. Fast encoding speed is essential for some steps and important in virtually all others.For real-time capture and Apple ProRes encoding of baseband video signals (either analog or digital SD or HD signal sources), Apple ProRes software encoders must be fast enough to keep up with the incoming real-time video frames. An appropriate video capture card must be used for this purpose, but otherwise no specialized encoding hardware is required to achieve real-time capture of baseband video to Apple ProRes formats.For file-based transcoding of video files that have been encoded with other(non-Apple ProRes) video codecs, transcoding to Apple ProRes entails both decoding of the starting technique and reencoding to Apple ProRes. The minimum total transcoding time will therefore be the sum of the time required to decode the file and the time required to reencode it to Apple ProRes. For certain video codec formats known to be highly complex and therefore relatively slow to decode, such as JPEG-2000 and the REDCODE® RAW (R3D) native codec format, the overall transcoding time will be dominated by the decoding time. Still, fast Apple ProRes encoding helps make the total transcoding time faster.Fast encoding and decoding also benefits rendering and exporting. Rendering effects, as part of a creative process or the final step before output, is basically a decodeof the source media and a reencode to the chosen final output format. During the rendering process, all of the decoding, blending, and compositing steps must be precomputed before encoding to the compressed format defined in your Final Cut Pro project. Although you can choose any Apple ProRes codec as a rendering format—from Apple ProRes 422 (LT) to Apple ProRes 4444—and change it at any time during post-production, Final Cut Pro X defaults to rendering in Apple ProRes 422.When rendering to Apple ProRes, the total rendering time is determined by the speed of both the decoding and encoding steps, which can be significantly quicker compared to other, more complex and slower codecs. The speed advantage of Apple ProRes is also beneficial when exporting a file at the end of a project. Ifyou need to deliver to the web, DVD, or Blu-ray disc, you can speed up the export process by choosing to edit in Apple ProRes instead of other professional formats, including uncompressed.Apple ProRes 4444 Alpha Channel SupportIn addition to supporting Y’C B C R or RGB 4:4:4 pixel data, the Apple ProRes 4444 codec type supports an optional alpha channel. The sampling nomenclature for such Y’C B C R A or RGBA images is 4:4:4:4, to indicate that for each pixel location, there is an alpha—or A—value in addition to the three Y’C B C R or RGB values. An alpha value specifies the proportion of its associated RGB or Y’C B C R pixel that should be blended with the pixel at the corresponding location of a background image, creating the effect of varying transparency for use in compositing workflows. Unlike Y’C B C R or RGB pixel values, alpha values do not represent samples of a real-world image, or even samples of a computer-generated image, both of which are intended for human viewing.Alpha values are essentially numeric data that specify how to blend, or composite,a foreground image into a background image. For this reason, Apple ProRes 4444 encodes alpha values exactly rather than approximately. This kind of exact encoding is called “lossless” (or sometimes “mathematically lossless”) compression. It uses different encoding techniques from those used by the Apple ProRes codec family for RGB or Y’C B C R pixel values, where approximate encoding is acceptable as long as differences from the original are not visible to the viewer and do not affect processing. The Apple ProRes 4444 codec losslessly encodes alpha channel values of any bit depth up to and including 16 bits.In summary, the Apple ProRes 4444 codec can be considered “visually lossless” for encoding the Y’C B C R or RGB pixel values intended for viewing, but “mathematically lossless” for encoding the alpha values that specify compositing. As a result, the degree of quality or fidelity is never a question for Apple ProRes 4444 alpha channels because the decoded data always matches the original perfectly.With any kind of lossless compression, the data rate varies according to the amount of image detail being encoded. This is true of Apple ProRes 4444 lossless alpha channel compression as well. However, in practice alpha channels typically contain just the information related to object outlines, so the optional alpha channel typically adds just a few percent to the overall Apple ProRes 4444 data rate. For this reason, the presence of an alpha channel in an Apple ProRes 4444 stream typically reduces decoding and encoding performance by only about 10 percent or less.。

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立体信息检测苹果表面缺陷(平行的结构光)

立体信息检测苹果表面缺陷(平行的结构光)

Q .Y A N G230 T he proposed system has been tested with samplea pples and the test results are presented .2.S egmentation of dark patchesD ark patches in apple images represent boths tems / c alyxes and patch-like blemishes , such asb ruises , wounds and many rots . They appear asc onnected regions of relatively small size and havev ariable shape and contrast . An example image iss hown in F ig .1 . When viewing the intensity image asa topographic relief , the patches appear as concavea reas in the grey-level surface .Therefore , they can bet reated as catchment basins in grey-level landscapes .B ased on this , a region-based segmentationa lgorithm , 5 called a flooding algorithm , has beend eveloped to segment the patches .Here , we explaint he underlying concepts of the algorithm in terms ofd igital elevation models ; the implementation detailsa re described in reference 5 .T he flooding algorithm is based on the concept ofw ater catchment basins . A catchment basin is a regiona ssociated with a minimum . To define the spatiale xtent of the region around the minimum , we considert he properties of water flowing over a topographicr elief . Water will always flow downhill until a localm inimum is reached . As more water is added ,ita ccumulates at the lowest points and gradually coverst he region surrounding the minimum , until it reaches ac ertain level at which the basin can no longer hold anym ore water and the surplus water spills over thel owest points of the basins’ rim .Thus , the basinb ecomes a lake .The corresponding elevation level ofF ig . 1 . A n example apple image with a blemish and a stema rea t he lake’s surface is called the spillover level .The s pillover level is used to delimit the spatial extent of a c atchment basin .Therefore , a catchment basin con- s ists of those points , which are connected to a m inimum and have a grey-level lower than the spillo-v er level . To detect all the dark patches ,we gradually flood the relief with water , all the catchment basins w ill be filled up and become lakes of various sizes and d epths . The lakes , which are equivalent to catchmentb asins , are the output of the flooding algorithm and r epresent the patches being detected . T he concept of a lake is extremely useful in that it h olds most of the characteristics desired in the follow-i ng classification . For instance , the surface area of a l ake is a clear indicator of the size of the detected p atch ;the average depth , i . e . the ratio of the water v olume to the lake surface area , is a measure mostly o f contrast , but contains gradient information as well . A nother obvious advantage of this presentation is that t he surface of each individual lake is always con-n ected . The segmentation result of the example image i n F ig .1 is shown in F ig .2 . The detected patches are h ighlighted with bright borders . T he algorithm has no tuning parameters , and the s egmentation output depends purely on image data . T here is little need for thresholding . It has no strong a ssumption about the contrast and shape of patches . T he sole working assumption used is that the patches a re not adjacent to the boundary of the fruit in i mages . Apart from the segmentation output ,some d escriptive parameters such as the minimum height ,c urrent volume and depth of the basins can also be c omputed as the flooding proceeds , and they are a vailable as outputs of the procedure .Once the F ig . 2 . R esults of patch segmentation for the image in Fig . 1 . D etected patches are highlighted by bright bordersA P P L E S T E M A N D C A L Y X I D E N T I F I C A T I O N W I T H M A C H I N E V I S I O N231p atches are segmented out,measurements of the c orresponding lakes can be made to extract other g eometric parameters.These parameters quantify the c haracteristics of each patch under consideration and a re ready to be used as inputs to the following c lassification step.3.T hree-dimensional shape information froms tructured lightT he surface of an apple can be divided roughly, a ccording to shape,into two basic parts,convex parts a nd concave parts.Most of the surface is convex and s ometimes nearly spherical.The concave parts are u sually in the areas around the stem and calyx.The s tem itself stands out from the middle of the concave a rea.On the other hand,blemishes,such as bruises a nd abrasions often occur on the convex surface of a f ruit.Therefore,the three-dimensional information a bout the dif f erence in surface shape can assist iden-t ification of the stem and calyx.A technique called l ight striping6i s used,in which the projected light p lanes and a camera make up an active stereo system s o that three-dimensional geometric information ab-o ut an object surface may be derived from optical t riangulation.F or fast processing,a set of evenly spaced parallel l ight stripes are projected onto the apple surfaces s imultaneously.An image with the stripes is grabbed f or each view of a fruit.This stripe image is obtained w ith the same camera as for the normal apple image w ithout the structured light.In stripe images,the s tripes on dif f erent parts of the surface have dif f erentF ig .3.A n example image with light stripes on the same apple a s shown in Fig .1. Stripes on dif f erent parts of the surfaceh a␷e dif f erent shapes s hapes.The stripes on convex surfaces are continuous a nd parabolic,and their curvature directions are m aintained.Stripes on nearly flat surfaces are almost p arallel.For concave surfaces around stem and calyx a reas,the appearance of stripes is complex.Stripes are n ot always parallel,can touch adjacent stripes due to s harp changes in depth,and can appear broken due to o cclusion.In the case of the continuous stripes,their c urvatures change sign when the surface changes from c onvex to concave.The stripes across stems are b roken owing to the depth discontinuity.F ig .3shows a n example image with light stripes on the same apple a s in F ig .1.T he correspondence between the shape of the s tripes and the shape of surface parts,provides the n ecessary three-dimensional information and this can b e obtained from the analysis of the dif f erent shape p atterns of curved stripes.Since stems and calyxes a ppear as patches in images,the analysis is directed to t he local area around each patch,which is first s egmented out with the flooding algorithm.Each s egmented patch in the apple image has a correspond-i ng area at the same location in the stripe image. T herefore,a rectangular window is placed around this a rea in the stripe image.The derivation of the t hree-dimensional information is thus reduced to the e xtraction of stripe patterns within each window.Note t hat in this way,the time- consuming reconstruction of t he actual three-dimensional geometric surface and t he associated calibration,as in the usual application o f light striping,are avoided.A principal parameter for describing the shape of t he stripes is curvature.On a convex surface,the s tripes are continuous and therefore have smoothly c hanging curvatures which may keep the same sign.If t he curvature of the stripes within a window varies d ramatically and changes sign,then the surface inside t he window should normally be a stem or calyx area (see discussion section for other cases).Before the c urvature is calculated,the stripe image is binarized b y a local thresholding process and then the stripes a re thinned to one pixel wide skeleton curves by a s hape preserving thinning method.7T he stripes are t hen represented by their skeleton curves,from which t heir curvatures are computed.The thinning result of F ig .3is shown in F ig .4.T he normal curvature calculation for planar digital c urves is notoriously inaccurate.Here we use a c urvature estimator for the skeleton curves.Let xϭx[s(t)]a nd yϭy[s(t)] be a skeleton curve in its p arametric form,where s is the arc length of the curve a nd is represented as a linear function of parameter t,sϭs(t).Let ␺(s) represent the tangent angle of the c urve at s.The point curvature k o f the curve isQ.Y A N G 232F ig .4.T he thinning result of Fig .3d efined as the instantaneous rate of change of ␺w ith r espect to arc length sk(s)ϭd␺(s)/d s(1) w here␺[s(t)]ϭt anϪ1y᝽(t)x᝽(t)(2)T o reduce the noise caused by taking the second-order d erivatives in the usual calculation of curvature,we u se the following practical method to approximate the c urvature.Let the increment ds be a small constant, t hen the dif f erenced␺(t)Ϸ␺(tϩ1)Ϫ␺(t)(3) c ontains curvature information.We,therefore,define t he dif f erence as an estimator of the point curvaturek␺ϭ␺(tϩ1)Ϫ␺(t)(4) T o reliably calculate ␺i n Eqn (4),we smooth the c urve,xϭx(t)and yϭy(t),with a Gaussian kernel g(t,␴)o f standard deviation ␴.The smoothed curve, X(t,␴)a nd Y(t␴),is expressed asX(t,␴)ϭx(t)ءg(t,␴)ϭ͵3␴Ϫ3␴x(u)1␴42πeϪ(tϪu)2/2␴2d u(5) Y(t,␴)ϭy(t)ءg(t,␴)ϭ͵3␴Ϫ3␴y(u)1␴42πeϪ(tϪu)2/2␴2d u(6)w here u i s a dummy integral variable and ءi s the s tandard symbol for convolution.Their first deriva-t ives are expressed asX᝽(t,␴)ϭx(t)ءѨg(t,␴)Ѩtϭ͵3␴Ϫ3␴x(u)Ϫt␴342πeϪ(tϪu)2/2␴2d u(7) Y᝽(t,␴)ϭy(t)ءѨg(t,␴)Ѩtϭ͵3␴Ϫ3␴y(u)Ϫt␴342πeϪ(tϪu)2/2␴2d u(8) S o ␺(t) in Eqn (4) is computed as␺(t)ϭt anϪ1Y᝽(t)X᝽(t)(9)N ote that whilst the angle function ␺(t) in Eqn (9) is m ultiple-valued,under practical constraints on con-t inuity of the derivative,it is uniquely defined.4.F eature selectionI n this study,the features are intuitively determined t o provide reasonable coverage of the feature space. O nce a small patch is segmented out from an apple i mage,some features of the patch are already avail-a ble,which include the lowest grey-level I l,the highest g rey-level I h,the area A,the boundary length C p a nd t he water volume V o f the lake representing the patch. O ther features,including curvature,are extracted f rom both the apple image and the stripe image.F or the curves within a focusing window,some s tatistics of curvature are computed.These are ave-r age positive and negative curvatures,K p a nd K n,the a verage absolute curvature ͉K͉,and the frequencies of p oints with positive,negative and zero curvature,F p, F n a nd F z,respectively.These statistics describe the c oarse distribution of the curvature.A n important descriptor for the shape patterns of s tripes is the similarity between the shapes of curves. T he stripes crossing a convex surface have greater s imilarity (i.e.are more parallel),than those crossing s tem and calyx areas.The similarity can be measured b y the correlation of curvature between curves.Ther-e fore the average correlation coef ficient R c i s selected. T he length of the analysed curves within a window is a g ood indicator of their continuity,which is strongly r elated to blemishes on convex surfaces.Hence,the a verage curve length L m i s included as a feature.On t he other hand,the relative number of short curveA P P L E S T E M A N D C A L Y X I D E N T I F I C A T I O N W I T H M A C H I N E V I S I O N233s egments within a window is a clue to the irregularity o f the stripes.The short curve segments refer to the s mall curves that are not long enough for computing t heir smoothed curvature.They usually occur on thec oncave stem and calyx areas where stripes ared iscontinuous owing to sudden change in depth.So, t he relative number of short curve segments S s i s c omputed.I n apple images,when a standing stem appears as a p rotrusion or the small spikes of a calyx appear on theb oundary of a patch,the boundary is likely to be morec omplex than that of a blemish.Hence,the compact-n ess P o f the patch is selected,which is defined as the r atio of the squared perimeter C p2to the area A,i.e. PϭC p2/A.The perimeter C p i s simply the count of p ixels on the patch boundary.Two shape measure-m ents of the water volume of the lake are also s elected.These are the average depth h a o f the v olume,defined as V/A,and the ‘‘conical’’ angle ␣o f t he volume,which is defined as the average radius d o f t he boundary to the lake’s centre divided by the m aximum depth of the water,i.e.d/(I hϪI l).To c ompute this latter measure,the geometric centroid of t he lake surface is found by the first moment method. T o complete,the primary intensity properties,mean I m a nd variance I␷o f the grey-level in a patch,are i ncluded.A ltogether,eighteen features are extracted for each p atch and are summarized as follows.F rom the stripe image :K p a verage positive curva-t ure,K n a verage negative curvature,͉K͉a verage a bsolute curvature,F p f requency of points with posi-t ive curvature,F n f requency of points with negative c urvature,F z f requency of points with zero curvature, R c a verage correlation coef ficient of curvature bet-w een curves,L m a verage curve length,S s p ercentage o f short curve segments,F rom the apple image :I m a verage grey level inten-s ity,I␷v ariance of grey level intensity,I l l owest grey l evel of the patch,I h h ighest grey level of the patch,A a rea of the patch,P c ompactness of the area,V w ater v olume of the lake,␣c onical angle of the volume,h a a verage depth of the volume.A s can be seen,these features are not independent.B ut in this study,we do not address the issue of s alient feature selection.Since the dependencies are n ot easily modelled and the features are not dif ficult t o compute,we retain all the features for the following c lassification.5.C lassification of patches with neural networksO nce the feature vector is extracted for each patch, s tems and calyxes can be identified by feeding the f eatures into a classifier and classifying the patch as e ither blemish or stem/c alyx,which are two possible o utput classes.The input feature data to the classifier p ossess some nonlinear relations to their correspond-i ng surface types.F ig .5a s hows a set of average p ositive and negative curvature data for two types of p atches.F ig .5b s hows mean intensity and variance. T he data are from the first group of samples (Training1).It can be seen from these figures that the twoc lusters representing the two classes are not linearly s eparable for these variables.A non-linear classifier is r equired.I n this work, a multi-layer feedforward neural n etwork was constructed for the classification.Three d if f erent network configurations,(18 :10:2),(18:27:2) a nd (18:10:6:2) were tested.No attempt was made to find an optimal network configuration and it might be p ossible to reduce the complexity of the net.The n etwork was fully connected.The number of nodes in t he input layer was the same as the number of input–0·1–0·2–0·3–0·4Averagenegativecurvature0·000·050·100·150·200·25Average positive curvature(a)(b)0·70·60·50·40·30·20·10·00·00·20·40·60·81·0Average grey-levelVarianceofgrey-levelintensityF ig .5.D istribution of two classes in feature sub -s pace .(a) S cattergram of a␷e rage positi␷e cur␷a ture ␷e rsus a␷e rage n egati␷e cur␷a ture for blemish and stem/c alyx ;(b) s cattergram of mean grey -l e␷e l intensity ␷e rsus ␷a riance of g rey -l e␷e l intensity for the two types of patches .ᮀ,blemishes ;ϫ,stalks/c alyxesQ.Y A N G 234f eatures (18).The output layer of the neural network w as composed of two nodes to correspond to the two o utput classes.The hidden layer contained ten nodes i n the first configuration,27 in the second.The third c onfiguration had two hidden layers with ten and six n odes respectively.The nodes in the output layer and h idden layer(s) had a non-linear transfer function of s igmoidal shape.The network was trained with a m odified back-propagation learning algorithm.8T he a lgorithm accelerated the learning by changing the t wo coef ficients used in the conventional back-p ropagation algorithm,learning rate and momentum f actor.D uring the training,the weights of the network w ere updated after each pass through all the training s amples.The desired outputs for each nodes in the o utput layer were arranged in an on –o f f manner,i.e. (1,0) represented blemish and (0,1) represents s tem/c alyx.The convergence of the learning was j udged by two conditions :whether the mean squared e rror for all training samples was smaller than a c riterion and whether the output errors for each t raining sample were all smaller than another c riterion.6.E xperimental resultsT he proposed techniques were tested with Golden D elicious and Granny Smith apples obtained from two p ackhouses in southern France.The apples were g raded in terms of surface defects by skilful inspec-t ors.Table 1 lists the number of sample fruits from e ach variety and grade.T he experimental arrangement for image acquisi-t ion comprised a CCD monochromatic camera (Pana-s onic,model WV-CD20),a projector for structured l ighting,and a light chamber for dif f use lighting.The a ngle between the optical axes of the camera and the p rojector was 20Њ.The light stripes were formed by p rojecting light through an optical grid of thin tran-s parent lines.The camera was mounted above the topT able 1S ample applesG radeA pple ␷a riety E xtra I I I I II T otalG oldenD elicious1214462698G ranny Smith810382480o f the lighting chamber.Apples were placed in the m iddle of the field of view.The orientation of the s ample fruits was chosen in a random manner.For t he blemish-free fruits from the Extra grade,the s tems/c alyxes were made visible to the camera.The b ackground of the viewing field was black.The video s ignal of the camera was sent to a transputer-based f rame grabber,where it was digitized into video m emory with 512ϫ512 spatial resolution and 8 bit g rey-level resolution.All images were stored and s hown in this paper in the size of 256ϫ256 pixels.T wo images were acquired for each view of an a pple.One was under dif f use illumination,another w as under structured light.A few apples had more t han one blemish on dif f erent sides of the fruit,hence m ultiple pairs of images were recorded for each of t hese apples.The flooding algorithm was applied to s egment out dark patches from apple images.For each s egmented patch,the selected 18 features as described i n the previous section were extracted from both i mages.The type of the patch,i.e.blemish or s tem/c alyx,was recorded together with the features, t o form a labelled sample.As shown in Table 2,a t otal of 274 samples were collected and arranged in f our sets.Two of them were training samples,the o ther two were used to test the performance of the t rained neural network classifiers.There were no t raining samples included in the test sample sets.The b lemish category included bruise,russet,scab,wound, i nsect bite,sun-burn and rot.T he extracted features had very dif f erent ranges of v alues,so to input them to the neural networks,the f eature values were normalized to the range of [0,1и0].The neural networks were implemented on a t ransputer system.In all the trainings the initial l earning rate and momentum factor were fixed at 0и1 a nd 0и5 but the training algorithm employed was not v ery sensitive to these initial values.The weights of t he networks were all initialized with random numbers i n the range of [0,1и0].The convergence criteria of the n eural network during learning were set to be less t han 0и001 for mean squared error and 0и01 for a bsolute output error of each training sample.These v alues are relatively small and were chosen to allow a ccurate learning with little loss of the generalizationc apability of the networks.All trainings with somed if f erent sets of initial weights converged rapidly, w ithin about 100 to 800 iterations.I n the performance evaluation for the trained neu-r al networks,a range of 0и3 was set in the interpreta-t ion of classification outputs,instead of the crisp o n-of f outputs (1,0) and (0,1).That is,a node in the o utput layer was regarded to be ‘‘on’’ if its output v alue was larger than 0и7,‘‘of f’’ if less than 0и3,A P P L E S T E M A N D C A L Y X I D E N T I F I C A T I O N W I T H M A C H I N E V I S I O N235T able 2D if f erent types of samplesA pple ␷a riety S amplesB lemish S tem/c alyx S ubtotal T otalG olden Delicious T raining 1473784T est 1442569274G ranny Smith T raining 2511566T est 2411455‘‘unknown’’ if between 0и3 and 0и7.The pattern p resented at the input layer was classified to belong to t he category that the ‘‘on’’ node represented.Thus,if t he outputs of the two nodes in the output layer were i n the range (1и0–0и7,0и3–0и0),the input patch was a ccordingly classified into the blemish category,and if t hey were in (0и0–0и3,0и7–1и0),the stem/c alyx c ategory.If both nodes were either ‘‘on’’ or ‘‘of f’’ and i f a node had ‘‘unknown’’ output,the input patch was c lassified into the unknown category.The selection of r ange 0и3 was conservative and imposed a strict r equirement for the networks.The range could be r elaxed to a slightly greater value.T he classification results are listed in Tables 3 and 4. T able 3 shows the classification accuracy in each test. T he accuracy was computed by dividing the number of c orrectly classified samples by the number of samples i n the test group.Table 4 shows the number of c lassification errors.Error I r efers to the misclassifica-t ion of stem/c alyx as blemish,Error I I i s vice versa,a nd Error I II r efers to the case in which either ab lemish or stem/c alyx input patch is classified into the u nknown category.It can be seen from Table 3 that t he classification accuracy of the three network con-figurations is about the same.As shown in Table 4, t he number of errors in the unknown category is s lightly reduced in the case of Test 1 with the network c onfiguration (18:10:6:2) and the case of Test 2 with t he network configuration (18:27:2).Since the first c onfiguration with 10 nodes in the hidden layer has m uch less complexity and its performance is accep-t able,it is to be preferred.The same number of errors i n Errors I a nd I I f or dif f erent configurations indicatesT able 3C lassification accuracy of the trained neural networksC lassification accuracy (%)N etworkc onfiguration T est 1T est 218:10:295и7 94и518:27:295и7 96и418:10:6:297и1 94и5T able 4C lassification errors of the trained neural networksN umber of errors S ampleg roup E rror type(18:10:2)(18:27:2)(18:10:6:2)T est 1I 0 0 0I I 1 1 1I II 2 2 1T est 2I 1 1 1I I 1 1 1I II 1 0 1t hat one or two specific pattern samples have feature v alues in the range of the ones for the other class.In t hese cases,persistent errors would occur.On the w hole,good classification accuracies have been a chieved.7.D iscussionT he technique developed in this paper is based on a n assumption that blemishes only occur on convex or flat surfaces of apples and therefore the light stripe p atterns on them reflects the type of the surface.It c an cope with the cases in which the blemished s urfaces are slightly indented.But,if the surface of a b lemish has a significantly concave shape as in cases of s evere mechanical damage,or a blemish occurs on the c oncave areas around stem or calyx,the shapes of the s tripes on the blemish will be similar to those on s tem/c alyx areas and the system will fail.Another l imitation is that if a dark patch occurs adjacent to the b oundary of a fruit in a view,the segmentation p rocess will not output it and no subsequent classifica-t ion will be made.However,in practical implementa-t ion,multiple views of a fruit are expected,so there is a good chance that the patch will not be adjacent to t he boundary of the fruit in one of the views.A n advantage of the technique is that,since onlyQ.Y A N G 236q ualitative features of stripe patterns are required, t here is no need for conventional three-dimensional s urface fitting and the calibration for the triangulation o f the structured lighting system is eliminated.This w as deliberately avoided because the complex and u npredictable stripe patterns,especially those caused b y out-standing stems,may present dif ficulties to those fitting techniques.T he technique has potential for high speed im-p lementation because of its simplicity.The necessary t hree-dimensional information is reduced to simple c onvexity or concavity,which is represented by the s hape pattern of the stripes.All segmented patches a nd all stripes on each patch can be analysed in p arallel.In the experimental arrangement,the two i mages are grabbed in sequence.If the wavelength of t he structured light is chosen in the appropriate s pectral range,the two images can be grabbed simul-t aneously by two sensors.9I t is also worth pointing out t hat the technique has the potential to be used for o ther fruits.8.ConclusionA n image analysis technique for the identification of a pple stems and calyxes has been developed.Struc-t ured light is used to provide the necessary three-d imensional shape information of apple geometric s urfaces.The analysis is focused on dark patches of f ruit surfaces,which are first segmented out by a flooding algorithm.For each patch,the qualitative f eatures of the stripe pattern are extracted from the s tripe image and the two-dimensional intensity pro-p erties are extracted from the apple image under n ormal dif f used light.When the three-dimensional a nd two-dimensional information is fused with a m ulti-layer feedforward neural network,the segmen-t ed patches are classified as stem/c alyx or blemish.T he proposed technique was tested with samplea pples and an average identification accuracy of 95%w as achieved.This result demonstrates that the system c an ef f ectively identify stems and calyxes which ares urrounded by concave surfaces,and distinguish them f rom discoloured blemishes which occur on convex or flat surfaces.A cknowledgementT his work was partly funded by the EC (CAMAR : 8001-CT91-0206).The author would like to thank P rof.A.K.Thompson at Cranfield University and Dr R.D.Tillett for valuable discussion.R eferences1W olfe R R ;Sandler W E A n algorithm for stem detection u sing digital image analysis.Transactions of the ASAE 1985,28 :641 –6442M iller B K ;Delwiche M J P each defect detection with m achine vision.ASAE Paper 89 –60193Y ang Q F inding stem and calyx of apples using structured l puters and Electronics in Agriculture 1993,8:31 –424Y ang Q C lassification of apple surface features using m achine vision and neural puters andE lectronics in Agriculture 1993,9:1–125Y ang Q A n approach to apple surface feature detectionb y machine puters and Electronics in Agri-c ulture 1994,11 :249 –2646B allard D H ;Brown C M C omputer vision,EnglewoodC lif f s,Prentice-Hall,1982,pp.52 –547G onzalez R C ;Woods R E D igital image processing,A ddison-Wesley Publishing Company,1992,pp.492 –4948C han L W ;Fallside F A n adaptive training algorithm forb ack propagation puter Speech and Lan-g uage 1987,2:205 –2189C rowe T ;Delwiche M R eal-time defect detection in fruit.I nternational Conference on Agricultural Engineering(AgEng’94),Milano,Italy,29 August –1st September 1994,Report No.94-G-027。

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越狱后改字体、换主题等常用文件路径汇总

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越狱后改字体、换主题等常用文件路径汇总之前我们介绍了修改微信提示音的方法，在越狱状态下除了微信提示音，你还可以更改 iPhone 字体、QQ/微信主题、软件启动页画面、解锁图标等等。

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越狱后使用爱思助手即可直接访问系统文件：系统文件比较多，如果不知道路径就无法进行替换，以下汇总了部分常用文件路径，建议收藏以便查找：系统路径来电铃声/Library/Ringtones短信铃声/System/Library/Audio/UISounds壁纸iPhone：/Library/Wallpaper/iPhoneiPad ：/Library/Wallpaper/iPadlivephoto 壁纸iPhone：/Library/Wallpaper/Motion/iPhoneiiPad ：/Library/Wallpaper/Motion/iPad相册/var/mobile/Media/DCIM截屏图片/var/mobile/Media/DCIM/999APPLE录像文件 video recorder/var/mobile/Media/iphone video Recorder中文字体/System/Library/Fonts/LanguageSupport/PingFang.ttc如果替换字体后原生键盘字体未变化，在路径：/var/mobile/Library/Caches/ 下找到 com.apple.keyboards 删除并注销即可生效。

英文字体/System/Library/Fonts/CoreUI/System/Library/Fonts/CoreAddition/System/Library/Fonts/Core如果字体文件没有会包含全文件夹文件，替换将文件夹对应替换，在不确认时直接覆盖所有文件夹可能会导致字符显示乱码。

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ipad 产品介绍


Glee 凭借热门电视剧中的歌曲和你身后的 Glee 合唱团，你 将通过 iPad 发现自己真正的明星潜质。下面是使用方 法：你来演唱喜爱的歌曲，Glee 合唱团为你和声。如 果你有点走调，他们还能纠正你的音准。你可以边唱 边录，再听听自己发挥得如何。然后，在 Facebook 和 Twitter 上分享你的歌曲，还能结识其他 Glee 歌 迷。通过将自己展现于世界面前，你可以赚取获得免 费歌曲的点数。这也是以完美音准演绎 Glee 歌曲的 绝佳方式。
欢迎来到应用程序的崭新世界。App Store 中已有数 千个专为 iPad 打造的全新应用程序，从游戏、教育 到生产力一应俱全。它们完全不同于你以前见过或用 过的应用程序，而这仅仅是个开始。
Popular Science+ 能够远远超越印刷纸张，对于杂志而言是令人惊叹的新景象。Popular Science 已经针对 iPad 进 行了彻底重新构思，因此，手指轻触一下就能让这款杂志应用程序上的一切展现活力。宽大的高分 辨率显示屏让照片更显生动，报道更引人入胜。全新的浏览方式令每一期刊物都更有趣。轻点一下 让文字消失，你就可以专心欣赏那些精彩的图像。要专注阅读报道，只需再轻点一下，文字就会出 现。四处轻点可在不同版块间切换，还可横向或纵向轻扫翻阅页面。你可以将期刊下载到 iPad，想 存多久就存多久，甚至可以将过期期刊保存在资料库中。
Marvel Comics 现在你可以在 iPad 上阅读 Marvel 作品，漫画从 此大不相同。iPad 屏幕上的色彩和图画如此惊艳， 带给你身临其境般的生动体验。开合双指缩放每一 页，以便浏览文字和图画。轻点缩略图来翻阅多个 页面。随着故事的展开，iPad 还可带你按分画面浏 览。也可以直接在 iPad 上购买漫画。按系列浏览 数百部漫画。在购买之前进行预览，然后下载单行 本或整个系列。甚至可订购印刷版本，放到手袋或 书架供你收藏。