1 PUC–Rio — Departamento de Matemática — Rio de Janeiro — Brazil

Hierarchical Isocontours Extraction and Compression

T HOMAS L EWINER 1,2,L UIZ V ELHO 3,H ′E

LIO L OPES 1AND V INICIUS M ELLO 31

PUC–Rio —Departamento de Matem′a tica —Rio de Janeiro —Brazil

2

INRIA —G′e om′e trica Project —Sophia Antipolis —France 3

IMPA —Laborat′o rio Visgraf —Rio de Janeiro —Brazil

Abstract.In this work,we introduce a new scheme to extract hierarchical isocontours from regular and irregular

2D sampled data and to encode it at single rate or progressively.A dynamic tessellation is used to represent and adapt the 2D data to the isocontour.This adaptation induces a controlled multi–resolution representation of the isocontour.We can then encode this representation and control the geometry and topology of the decoded isocontour.The resulting algorithms form an ef?cient and ?exible isocontour extraction and compression scheme.Keywords:Level sets,Data Compression,Simplicial Methods,Progressive Transmission,Geometry

Processing.

(a)1619bytes (b)4024bytes

Figure 1:Progressive compression of the cortex of a Com-putational Tomography image,topology controlled.1

Introduction

Curves are one of the basic building blocks of geometry processing.They are used to represent shape in 2D im-ages,terrain elevation on maps,and equations in mathemat-ical visualization.In most of those applications,the curves can be interpreted as an isocontour of a 2D dataset,possi-bly mapped on a more complex space.Those isocontours are ?exible objects that can be re?ned or reduced,that can deform with differential simulations or mathematical mor-phology,and that can be described for shape classi?cation or automatic diagnostic in medicine or geosciences.Problem statement.Given the sampling ?f

of scalar func-tion f de?ned over a domain D embedded in R 2(such as a 2D image or a discrete surface),the isocontour of an iso value αis the curve f ?1(α).Such an isocontour corre-sponds to only a small part of D ,but usually covers a large area of the domain.For example,the cortex corresponds to only speci?c x–ray scintillation inside the scan of the whole head (see Figure 1),the elevation curve is only a small part inside a topographic map (see Figure 2).Therefore,speci?c

compression techniques for isocontours should provide bet-ter compression rate than the encoding of the entire 2D data.Contributions.In this paper we introduce a new method for extracting and compressing isocontours based on a dy-namic tessellation of the 2D data.This structure shows very nice adaptation properties,allowing extraction of the isocontour with different level of details.The main idea is to encode the tubular neighbourhood of the isocontour extracted from different levels of detail of the tessellation.Moreover,the adaptation the tessellation can depend on the isocontour,providing to our compression scheme a full con-trol on the geometry and topology of the decoded isocon-tour.The resulting algorithms are ?exible,can handle ir-regular 2D data,single–rate and progressive transmission together with uniform and adapted re?nements.2Related work

In this work,we will use dynamic adaptive triangulations to represent and encode two–dimensional isocontours.This section describes some relevant works related to dynamic adaptive tessellations,and hierarchical isocontour extrac-tion and isocontour compression.

Adaptive Tessellations.Hierarchical data structures are traditionally used for progressive compression and visual-ization of images.The usual representation for images re-lies on a rectangular grid that is subdivided uniformly or adaptively with a quad–tree.However,these structures are restricted to rectangular data sets.The size of those rect-angles reduces twice as fast as the sizes of triangles in tri-angular tessellations,resulting in less adaptability.We will therefore focus on triangulations.[15]introduced multi–triangulations as a general concept for adapted variable res-olution simplicial structures.[14]developed a binary multi–

(a)21

bytes(b)31

bytes(c)46

bytes(d)156

bytes(e)690bytes

Figure2:Progressive compression of an elevation curve of the Sugar Loaf.The tessellation is adapted to the curve to minimize the distortion and to preserve the topology.(a)The shape of the tubular neighborhood of the curve is sent?rst, with the nodes sign.(b,c,d)Then the re?nements of the tessellation are encoded with the signs of the new nodes.(e)Finally, the values of the nodes in the tubular neighborhood are re?ned.

resolution structure based on stellar operators,which is a multi–triangulation with optimal properties.[18]proposed

a very simple scheme for dynamically adapting triangula-tions while maintaining regularity conditions.

Hierarchical Isocontour Extraction.A hierarchical rep-resentation of an isocontour can be obtained by reduction of the polygonal curve of a single isocontour:[5]introduced the?rst algorithm for reduction of the polygonal approxi-mation of a curve in the plane.Since then,this algorithm has been extended and improved in many aspects(see[19] for guarantees on the consistency of the reduced curve).

Nevertheless,this hierarchy can be obtained by ex-tracting isocontours at each level of detail of a multi–resolution representation of the2D data[16,12].The approximation

of complex implicit curves usually requires robust compu-tation,whose result can be seen as an isocontour.Hierar-chical structures such as quad–trees usually provide simple and ef?cient solutions[11].Similarly,the hierarchical rep-resentation of the image data can be adapted to the speci?ed isocontour.For example[9]provides a hierarchy of rect-angles to represent the isocontour,using genetic algorithm

to optimize the dimensions of the rectangles.Those hier-archical representations are numerous when dedicated to a speci?c application,in particular for shape analysis[13,3]. Isocontour compression.Isocontour compression is usu-ally done as a compression of a non–self–intersecting curve, for example as two separate signals for each coordinate,or as a vector displacement[4,17].It can also be compressed by the popular chain code when the curve points are limited

to pixel quantization[6].In that case,it can be compressed as a2D signal[8,2].[7]introduced another concept by encoding a hierarchical representation of the isocontour in-duced by a multi–resolution of the2D data.The progres-sion tries to maintain the chessboard distance from the orig-inal curve to the encoded one.The coarser resolution is en-coded as two separate signals,and the position of the points introduced by the re?nements are encoded as a difference with the near-by points.

3Overview

In this work,we intend to encode a hierarchy of isocon-tours by their tubular neighbourhood.The tubular neigh-bourhoods are extracted from an adapted triangulation of the original2D data.The data structure we will use to rep-resent the2D data is the one of[14,18].We adapt it to the neighbourhood of the isocontour,and reduce it accord-ing to the isocontour topology,geometry and position in-side the triangulation.This structure allows to a very sim-ple multi–resolution isocontour extraction,and enables us to compress the curve at single rate or progressively.More-over,it is well suited for uniform and adapted progression on both regular and irregular2D data.It can prevent topo-logical changes or high geometric distortion during the pro-gression.Finally,it works with sub–pixel interpolation for the curve,which enables smooth curve reconstruction at any level of detail.

Paper outline.We will introduce the adaptive triangu-lation we used in Section4.This structure can represent regularly sampled data with the same amount of sample points as the classical quad-tree representation,but it can also adapt to irregular data.It also provides simple and effective controls on the topology and geometry of the iso-contour as explained in Section5.Our compression scheme is introduced in Section6,and the results are showed in Section7.

De?nitions.The2D data is given as a collection of sam-ples v i,each of which is associated with its isovalue?f(v i). Those samples are triangulated.In particular,when the samples are regularly spaced on a2D grid(a grey–scale

Figure3:A regular BMT adapted to an isocontour and the corresponding tubular neighbourhood.

image for example),this triangulation can be automatically generated by the subdivision of a two–triangles square.

Assuming that we want to obtain the isocontour cor-responding to the isovalueα,a sample of the2D data is classi?ed as positive or negative depending whether its iso-value is greater thanαor not.An edge of the triangulation is crossing when its end–points have opposite signs.A tri-angle is crossing when it contains a crossing edge.The tubular neighbourhood of

the isocontour is the set of all the crossing triangles(see Figure3).

From now on,the isocontour will be approximated by a polygonal line linking the isocontour points interpolated at each crossing edge of the tessellation.In the case of a grey–scale image,this corresponds to a linear sub–pixel in-terpolation(as the purple points of Figure3).The samples of the2D data will be referred as the vertices of the trian-gulation,as opposed to the points of the isocontour.

4Adaptive Tessellation

The multi–resolution structure we will use here is the Reg-ular Binary Multi–Triangulation(RBMT)[14,18].This structure is constructed using Stellar operators on edges and can decompose adaptively the2D data.These decompo-sitions can be regarded as hierarchies of conforming tri-angulations.In this section,we will give a brief descrip-tion of the Stellar operators on edges and the binary multi–triangulations.

4.1Stellar Operators

Stellar theory[1]studies the equivalencies between simpli-cial complexes(i.e.,a generalization of a triangulation)and de?nes topological operators that change structures while maintaining their integrity and coherence with the mod-elized object.

The stellar subdivision operation inserts a vertex into an r–simplex of an n–dimensional simplicial complex.The inverse of this operation is called stellar simpli?cation.Stel-lar theory states that stellar operators on edges are suf?cient to map any two equivalent manifolds[1].Edge–subdivision and Vertex–simpli?cation will be the basic operators to con-struct Binary Multi–Triangulations(see Figure4).Figure4:Subdividing an edgeσ simplifying a vertex w.

(a)curvature(b)distortion

Figure5:The isocontour of Figure3adapted according to

(a)the curvature and(b)the distortion.

4.2Binary Multi–Triangulation

The Binary Multi–Triangulation(BMT)is a multi–resolution structure based on stellar subdivision on edges.When sub-dividing an edge,its incident triangles are subdivided in two.Therefore,a sequence of subdivisions on edges can be represented as a binary tree structure,where each node represents a triangle and the two sons of a node t are the two triangles obtained by subdividing t.This binary tree(actu-ally a forest)adapts more nicely than the classical quad-tree for image decomposition.The level of a triangle is its depth in the binary tree.

The BMT is reduced or re?ned by walking up and down the binary tree,creating a hierarchy of triangulations at different resolutions.We perform those operations ef?-ciently by maintaining for each triangle t,the vertex w that has been inserted during the subdivision that created t.The vertex w is called the simpli?cation vertex of t,and the edge opposite to w is called the subdivision edge of t.For exam-ple Figure6shows the subdivision edges of the BMT as darker edges.

4.3Adaptation Properties and Regularity

The adaptability of the BMT comes from the possibility to re?ne and reduce the triangulation,while maintaining the dependencies between its triangles.In particular,we will maintain gradual transitions by preventing two adjacent tri-angles from differing by more than one level.To do so,it is necessary to propagate a subdivision or simpli?cation to adjacent triangles.The propagation of a subdivision on an

Figure6:RBMT adapted to the bold line:at each level,every triangle crossing the bold line is re?ned,but subdivisions in the upper–right part of the square propagate to the lower–left part.

edge e is performed by checking that each triangle t adja-cent to e has e as a subdivision edge.If it is not the case,a subdivision is performed on the subdivision edge of t.This subdivision can require other triangles to be subdivided if they do share their subdivision edges.The propagation of a simpli?cation on a vertex is done in a similar way.With this restriction,the resulting structure is called a regular binary multi–triangulation(RBMT).In that structure,subdividing a triangle means subdividing its subdivision edge,while simplifying it means

simplifying its simpli?cation vertex.

For example,Figure6illustrates a sequence of re?ne-ments adapting the triangulation to the bold line.In order to preserve gradual transition between resolution levels,lo-cal re?nements around the bold line propagates inside the triangulation(in this example,far away from the bold line), as what happens to the bottom left part.

5Isocontour Extraction

The isocontour is computed as a polygonal curve.Each crossing triangle t of the RBMT contains a segment[p1,p2] of the isocontour.The extremities p1and p2of the seg-ment are linearly interpolated on the two crossing edges v0v1and v0v2of t:p i=λ·pos(v0)+(1?λ)·pos(v i)with

λ=isovalue(v i)

isovalue(v i)?isovalue(v0).If the isocontour is extracted

from an image,the subpixel interpolation can generate an ambiguity.This ambiguity is solved(somehow arbitrarily) by the tessellation,since only triangular elements are inter-polated.The resulting isocontour is controlled redundantly by the isovalue of the vertices(grey level of the represent-ing pixels)and by the size and position of the edge.This double control provides more?exibility on the isocontour progressive compression.

Multi–resolution Representation.The representation of the isocontour corresponds to the polygonal interpolation on the edges for different resolutions of the RBMT.These levels are obtained by successive reductions of the?nest level.The reductions can be uniform,simplifying all trian-gles whose level is greater than a given threshold(see Fig-ure6).It can also be adapted to the topology of the curve.Figure7:The simpli?cation of w induces a distortion that can be measured as max{d(q,p 1);d(q,p 2)}.

For example,it is possible to simplify all the triangles that are not crossing,preserving the isocontour and its tubular neighbourhood(see Figure6.The re?nement can also be done in order to preserve small triangles where the curve has a high curvature,and to reduce the triangulation where it is more?at(see Figure5(a)).In a similar way,during compressing,we will be able to minimize the distortion of the curve and to preserve its topology(see Figure5(b)). Distortion control.The distortion induced by a simpli?-cation of a vertex w can be estimated by the encoder.To perform such a simpli?cation,the star of w needs?rst to be composed of only four triangles(see Figure7).The points of the reduced curve will be the interpolation of the isocon-tour on the remaining edges,with the isovalues of the ver-tices quantized according to the compression tuning.For example,in the case of Figure7,the distortion can be esti-mated as the maximal distance between q and p i. Topology control.Similarly,the topology of the isocon-tour can be easily controlled during the simpli?cation of a vertex w.Actually,there are a few prohibited simpli?ca-tions.The destruction of a connected component occurs when all the four edges incident to w are crossing.The separation of a connected component in two or the merge of two connected components happen when the subdivision edge v0v1is not crossing,while wv0and wv1were crossing edges.

(a)Location:level4, 1,L R L R.+??

.(b)Vertex signs:??

++++++

.

(c)2known vertices,

then:?+?

.

(d)????++?+

?End.

Figure8:Uniform encoding of the coarser resolution of a small sinusoid.The light curve is a second–order?tting of the decoder’s points(in the middle of the crossing edges),and serves as geometrical predictor.

6Isocontour Compression

The techniques we are now to introduce perform both direct and progressive encoding of the tubular neighbourhood of an isocontour.Moreover,the encoding of this neighbour-hood can be done uniformly(i.e.,the triangles of the tubu-lar

neighbourhood of the isocontour have constant size,see

Figure8)or adaptively(i.e.the size of the triangles varies according to the contour see Figure10).

This section is organized as follows.We will intro-duce our method for encoding the tubular neighbourhood of the isocontour?rst uniformly and then adaptively.These methods can be used to encode the coarser resolution of the progression,or to encode the entire isocontour at sin-gle rate.Then,we will detail our techniques to encode the re?nements,used for the progressive compression.Again, this process can be done uniformly or adaptively.Finally, we will explain the geometry encoding,and describe how to reuse the compressed data of one isocontour to encode other near–by ones on the same scalar data.

6.1Coarser Resolution

Our main idea is to encode the tubular neighbourhood go-ing along with the isocontour,from one crossing triangle to an adjacent one in the tubular neighbourhood.The algo-rithm encodes?rst the localization of an unvisited crossing triangle t0,and the signs of its vertices.From this initial tri-angle,it follows the isocontour,encoding the sign of each unvisited vertex encountered.When the traversal is done,it continues on the next connected component.Notice that the vertices of the only tubular neighbourhood have their iso-value encoded,leaving our algorithm almost independent of the initial size of the2D data size.

Localization.The location of a triangle t0can be encoded using the binary tree inherent to the RBMT.The root of the tree contains only a few triangles:2for a regular grid. The triangle containing t0is encoded by its index.Then, knowing the level l of the triangle to encode,it can be lo-Figure9:Coarser resolution compression:the traversal goes from t0to t1through the gate e0,and encodes the vertex v1.

calized using a sequence of l symbols Left and Right(see Figures8(a),10(a)and10(d)).The signs of its vertices are then encoded and all of them are marked as visited;the ini-tial triangle t0is also marked as visited.

Uniform encoding.Once the signs of the vertices of the initial triangle t0have been encoded,its crossing edges are known to the decoder.The?rst one is chosen to be the ?rst gate e0(see Figure9).The triangle t1(=t0)incident to the gate e0is also crossing.The sign of the vertex v1 opposite to e0is encoded,and both t1and v1are marked as visited.The algorithm then continues the same way starting as t1.The traversal ends when both triangles incident to the gate are marked,or when the gate is a boundary edge of the RBMT.

Actually,the sign of the vertex v1is encoded only when it has not been marked.This guarantees to encode exactly one sign bit per vertex,with an overhead of a few bits per connected component,used for the localization pro-cedure(see Figure8).

Adaptive encoding.The above algorithm can be easily modi?ed to encode the tubular neighbourhood of the iso-contour when it is composed of triangles of different lev-els.In that case,the algorithm also encodes the level of the current triangle(t1)during the traversal.The decoder will read the required level for t1and subdivide or simplify t1

(a)Location:level 5,0,L R L L L .?++

.

(b)Vertex levels and signs:>?=?<+

.

(c)=+>?>+>?=?<+

.

(d)level 5,trig 0,L L L L L .?++.>?=?<+

.

(e)=+>?>+>?=?<+End .

Figure 10:Adaptive encoding of the coarser resolution of a small hyperbola.

if necessary.The RBMT we use maintain a difference

of at most one level between adjacent triangles (see Section 4).Therefore,the decoder will have to subdivide or simplify an unvisited triangle at most once,and the encoder will man-age only 3symbols: = when the levels match, > or

< when the levels differ.The encoding of the signs is done similarly to the uniform one.

This guarantees to encode exactly one bit per vertex of the tubular neighbourhood,plus one symbol of {=,<,>}per triangle of the tubular neighbourhood,with an overhead of a few bits per connected components,used for the local-ization procedure (see Figure 10).6.2

Progressive re?nement

The re?nement methods allow a progressive adaptation of the tubular neighbourhood to the isocontour,using each time smaller triangles.The algorithm can simply encode the signs of the new vertices created by the subdivisions of all triangles on the tubular neighbourhood,or it can ?rst specify which triangles to subdivide and then send the sign of the new vertices inserted.

Uniform re?nement.The uniform re?nement simply sub-divides ?rst all the triangles of the tubular neighbourhood,and then en/decodes the signs of the vertices created by the subdivision inside the former tubular neighbourhood.The order of the new vertices is induced by the traversal used for the coarser resolution en/decoding.

When subdividing a triangle crossing the isocontour,the con?guration of its sub–triangles is determined by the sign of the new vertex introduced on the subdivision edge.This sign is encoded during the compression.This subdi-vision can locally extend the tubular neighbourhood (see Figure 11).This case occurs when a non–crossing subdivi-sion edge e =v 1v 2gives rise to two crossing sub–edges.In that case,the vertex v opposite to e will be included in the tubular neighbourhood,but its sign need not to be encoded as it can be deduced by the decoder as the sign of v 1(see Figure 11).

Figure 11:A subdivision can extend the tubular neighbour-hood of the isocontour.The sign of v is the one of v 1and v 2.

Adaptive re?nement.Our method also allows an adap-tive progression,creating smaller triangles where the iso-contour is more complex,and leaving bigger triangles where it is simple.For each subdivision edge in the tubular neigh-bourhood,we encode the Re?ne and Keep code.When a Keep symbol is encoded,the subdivision edge will not be considered in future re?nements,keeping it as a leaf on the hierarchy.The subdivision edges of the tubular neigh-bourhood are collected in the order of the coarser resolution traversal.The sign of the newly inserted vertices is encoded in the same way as for uniform re?nement.

Actually two successive resolution of the RBMT can differ locally by more than one level in the binary tree.Therefore,the above sequence will be repeated as long as a Cont/Stop bit is read by the decoder after the vertex signs are received.

6.3Final geometry encoding

Once the tubular neighbourhood of a resolution level is en-coded,only one bit for each vertex of the RMBT is known to the decoder.Therefore,the position of each point p of the isocontour is a priori the middle point of a crossing edge e .This can be improved by sending the isovalue of the endpoints of e .Moreover,this scalar value will be used to encode other isocontour generated with a different iso-value.

The input 2D data regularity can actually in?uence

(a)Location:level6, 1,L L L L L R.?+ +.

.(b)Vertex levels and

signs:=?>+

.

(c)level5,trig0,L L

L L L

.

(d)?+?.=?

=

?>+>?=?<

+=?.

(e)>?=?=

?=<+=?>

?=+<

Figure12:Adaptive encoding of the coarser resolution of a cubic:irregular tessellation can reduce the distortion. the quality of the compression(see Figure12).For spe-

ci?c applications such as human face contour compression,

the input data can be sampled according to the mean and

variances of the original isocontour,resulting in an elliptic–

radial initial distribution of the2D data.

6.4Close–by isocontour encoding

Once an isocontour C corresponding to an isovalueαC has been decoded,the decoder knows its entire tubular neigh-bourhood.It can be used to encode also isocontours C corresponding to isovaluesαC close toαC,especially for medical applications where the contrast of the2D data is not well de?ned.C can be easily encoded as a new coarser resolution by sending?rst the isovalueαC and then the quantized isovalues of the vertices not precise enough or unknown to the decoder.

7Results

The methods we introduced here are?exible and can be used in many ways.For isocontours of images,we chose to reduce the complete triangulation of the pixels.We then encoded the isocontour at each step of this reduction,which can be uniform or adapted to the isocontour.For implicit curves,we sent in–between two levels of detail a part of the geometry(2bits).We implemented our compression scheme with a context arithmetic coder of order0for the coarser level transmission,2for the re?nements and1for the?nal geometry encoding.We used a simple predictor based on a second–order curve?tting[10](see Figures8 and10).

Our method resulted in ef?cient rate/distortion curves (see Figure13).The different controls on the adaptation of the multi–resolution can have a signi?cant cost.For exam-ple,on regular models such as the elevation curve of the Sugar Loaf of Figure2,the distortion and topology con-trols provide nicer results to the eyes at the beginning of the compression than uniform re?nements,while?nally result-ing in similar performances.Topology control means an

(a)progressive:97bytes(b)direct:173bytes,

progressive:283bytes Figure14:Progressive compression of a car number plate (with enhanced contrast),topology controlled.

extra cost depending on the complexity of the model(com-pare the car number plate of Figure14with the brain model of Figure1on Figure13),but the rate/distortion perfor-mance has a similar evolution for all methods and models (see Figures13(a)).Geometry encoding seems a good al-ternative to re?nements for regular models such as the el-lipse of Figures13(a).Single rate encoding is,as usual,a little more ef?cient than progressive encoding,but can be complemented by the?nal geometry compression of Sec-tion6.3.

8Next steps

We introduced a complete isocontour multi–resolution ex-traction and compression scheme.The dynamic triangu-lation we used provides a very simple way of creating a multi–resolution structure of the2D data adapted to the iso-contour.This structure allowed us to achieve both direct and progressive compression,and to encode the isocontour uniformly or adaptively.Moreover,it provides a full con-trol on the progression,granting topological and geometri-cal control on the decoded isocontour.

We used binary multi–triangulations for the scalar data representation,implemented using dimension–independent generic programming[14].This offers a very simple ex-

0.01

0.1 1 10 100

1 10 100 1000 10000

d i s t o r t i o n : H a u s d o r f f d i s t a n c

e (% b b o x )

rate (bytes)

brain {Figure 1} (adapted to topology)

ellipse (alternate refine/geometry)sloaf {Figure 2} (adapted to distortion)

sloaf (uniform)

(a)Hausdorff distance 0.001

0.01

0.1

1

10

(b)Mean absolute error

Figure 13:Compression results:on complex models,topology control is mode expensive with progressive encoding.tension of our algorithm to compress level sets in any di-mension with a similar ef?ciency.This will require ?rst to optimize the arithmetic coding and prediction parameters we use for the compression,and to quantify the advantages of geometry encoding upon re?nement operations.References

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品牌营销策划方案

目录 1.市场环境分析 行业分析 自我分析 消费者分析 竞争对手分析 2.营销策略分析 营销战略目标 营销主题 营销思路 营销策划组合 3.创意设计提案 平面 媒体 4媒体投放提案 媒体投放目标 媒体投放选择 5.广告费用预算 市场环境分析 行业分析 近年来，我国酿酒行业发展态势良好，从今年一至三季度行业经济运

行状况来看，白酒、黄酒、葡萄酒增速均超过两位数，符合国家产业政策指向，全行业增长速度8.66％，与整个经济增长同步；从效益指标看，税金、利润的增长都在两位数以上，特别是利润指标涨幅达35.52％，创历史最好水平。预调酒作为新兴酒类，正在蓬勃发展着。目前，在我国预调酒市场上，活跃在前面的品牌有百加得“朗姆”预调酒，锐澳预调酒，龙舌兰预调酒等。 预调酒开始逐步扩大市场，有很多细分品类，如从口味上区分，可以分为蓝玫瑰、香橙、水蜜桃、青柠等各种纯天然果蔬汁口味；从内容物上分，可以分为白兰地、伏特加、朗姆酒、威士忌等。 产品自我分析 目前酒类市场在中国仍处于起步阶段，但根据RIO对日本、欧美等国预调鸡尾酒的研究，加之RIO在中国市场多年的经验，RIO正在不断引领市场格局。COCKTAIL传承了英国预调鸡尾酒的优质酿造工艺，并经过长期的研发，RIO瓶装、罐装鸡尾酒产品在中国市场中已占据半数市场。 缤纷口感：RIO预调鸡尾酒现已成功推出6种口味的瓶装产品，以及3 种口味的罐装产品。根据消费者对口味的不同偏好，RIO始终倾心调配，既有口感清爽的淡雅滋味，也有醇厚悠长的曼妙回甘，总有一款值得心仪。 全球选材：选材地道优质的酒基，才能成就不凡的口感，RIO的酒基有选自法国的干邑白兰地、精选的俄罗斯上等伏特加，以及采至波多黎各和古巴的清爽朗姆。 健康考量：饮酒的欢乐与健康的理念并不矛盾，RIO汇聚世界各地的优质水果，清纯甜美的白桃、香气浓郁的西柚、酸爽清新的柠檬，采用鲜榨冷冻技术提纯果汁。

RIO鸡尾酒市场调查报告

RIO鸡尾酒市场调查报 一、前言 此次选择的调查对象主要是18-40岁的在校学生及白领阶级等。 在全球化市场下，目前中国鸡尾酒市场刚刚兴起，各种品牌混战不清，消费者也不明确自己该选择哪款鸡尾酒。 但根据RIO对日本、欧美等国预调鸡尾酒的研究，再加上RIO在中国市场多年的经验，RIO正在不断引领市场格局。COCKTAIL传承了英国预调鸡尾酒的优质酿造工艺，RIO瓶装、罐装鸡尾酒产品在中国市场中已占据半数市场。 COCKTAIL源于法语单词“COQUETEL”，是由各种烈酒、果汁混合而成，含有适当的酒精成分，是一种能使人感到爽洁愉快的浪漫饮品。 RIO之所以倍受喜爱，只是因为那种似醉微熏，欲拒还迎的美妙感觉像极了爱情的滋味。ALCOPOP是ALCOHOL（酒精）与POP（流行）的组合，其诠释了后现代混合风格的内涵和“放弃游戏规则,只要人人都HIGH”的精神；它是属于年轻人的时尚至酷，代表不羁与个性、叛逆与张扬、率性与纯真、独立与自我。 消费者的想法： 随着对外交流的日益发展 , 鸡尾酒、洋酒在国内市场上的主要消费群体逐步扩大。现阶段 ,西方的消费文化以各种渠道在国内得到足量的传播 , 因此洋酒消费特别以红酒和预调鸡尾酒在国内的消费人群逐步扩大。个人消费者的消费能力逐年上升。这些消费群体大部分属于社交型和自我表现型的消费者 , 他们选择预调鸡尾酒是通过该酒的品牌价值来抬高自己 , 从而去证明自己属于某个特有的群体。 RIO鸡尾酒的策略： 抓住目标消费者的这种心理需求，通过调查分析，确定了以“青春百变，真我再现”为主题，以“真我rio，百变青春”“青春俱乐部 The young club"、balalala活动来进行品牌推广。将锐澳预调酒产品作为一种高品质生活的象征，向消费者传达一种全新的百变时尚生活方式，通过与目标群体展开互动，使消费者对锐澳预调酒留下更深刻的印象。从而赢得目标消费人群的信任和一定的区别意识，并相互传播，最终达到提高品牌知名度的效果，建立“正宗预调鸡尾酒=锐澳”的定位形象。 二、品牌历史与产品由来 （一）品牌由来： RIO锐澳鸡尾酒源于巴西著名城市“里约热内卢(RIO DE JANEIRO)”的简称，寓意充满活力、时尚、热情、阳光、快乐、自在的性格 生产公司 RIO锐澳鸡尾酒自成立伊始，就致力于中国预调鸡尾酒市场的开拓与建立，并在2007年，产品即已覆盖全国市场。目前，RIO锐澳鸡尾酒作为国内唯一一家专业生产鸡尾酒的企业，现已成为中国预调鸡尾酒市场的领军企业。伴着整个市场逐步成熟，RIO锐澳鸡尾酒势将书写预调鸡尾酒在中国的下一个传奇。 （二）产品特色 缤纷口感：RIO预调鸡尾酒现已成功推出6种口味的瓶装产品，以及3种口味的罐装产品。根据消费者对口味的不同偏好，RIO始终倾心调配，既有口感清爽的淡雅滋味，也有醇厚悠长的曼妙回甘，总有一款值得心仪。

RIO鸡尾酒广告策划书

广告策划书 Rio锐澳鸡尾酒 课程名称：广告策划 班级：15广告学（专升本） 组员：陈泽贤（005）王煜彬（024) 魏虹倩（026）徐杰（028） 庄金辉（036）李华玲（050） 吴惠琳（062） 2016 年4 月13 日

目录 1．广告的市场环境分析 (5) 1.1宏观环境分析 (5) 1.2行业分析 (5) 1.3对手分析 (6) 1.4产品分析 (7) 1.5消费者分析 (8) 1.6 SWOT分析 (10) 2．广告策划 (11) 2.1广告目的 (12) 2.2目标市场 (12) 2.3广告定位 (12) 2.4广告卖点 (12) 3.广告活动 (13) 3.1终端促销活动 (13) 3.2公关推广活动 (14) 3.3新媒体营销活动 (15)

4.广告创意作品 (16) 4.1广告视频脚本 (16) 4.2平面广告设计图 (18) 5.媒介策划 (19) 5.1媒介分析 (19) 5.2媒介目的 (22) 5.3媒介选择与组合 (22) 5.4媒介排期 (28) 5.5媒介投放表 (28) 5.6媒介投放排期 (29) 6.费用预算 (31) 6.1活动经费预算 (31) 6.2媒介广告费用 (32) 7.广告效果评估 (33) 8.结束语 (33) 9.附录 (34) 9.1锐澳鸡尾酒市场调査问卷 (34)

前言 品牌由来：RIO鸡尾酒源于巴西著名城市“里约热内卢”（RIO DE JANEIRO)的简称，寓意充满活力、时尚、热情、阳光、快乐、自在的性格。RIO锐澳鸡尾酒自成立伊始，就致力于中国预调鸡尾酒市场的开拓与建立，并在2007年，产品即已覆盖全国市场。RIO锐澳鸡尾酒作为国内唯一一家专业生产鸡尾酒的企业，现已成为中国预调鸡尾酒市场的领军企业。伴着整个市场逐步成熟，RIO锐澳鸡尾酒势将书写预调鸡尾酒在中国的下一个传奇。为了全面提高RIO鸡尾酒在中国消费者中的认知度和美誉度，进而扩大RIO鸡尾酒在中国的市场份额，提高RIO鸡尾酒的市场占有率和市场覆盖率，自4月9号以来，我们经过认真的市场调研，经过多次的商讨论证形成思路整理成文案策划书。

锐澳

市场营销策划书 目录 1.执行概要 (5) 1.1企业简介 (5) 1.2营销背景 (6) 2.目前营销状况 (7) 2.1中国酒类市场现状 (7) 2.2中国预调酒市场的发展特点 (7) 2.3产品营销状况 (8) 2.3.1产品分析 (8) 2.3.2消费者分析 (10) 2.4竞争状况分析 (10) 2.4.1竞争态势 (10) 2.4.2主要竞争对手产品分析 (11) 2.4.3竞争综述 (12) 3.0 SWOT分析 (18) 3.1优势 (18) 3.2劣势 (18) 3.3机遇 (18) 3.4威胁 (19) 3.5营销建议 (19) 4.0财务目标 (16) 5.0营销战略 ..................................... 错误！未定义书签。 5.1营销目标 (16) 5.2营销主题 (17) 5.3营销对象 (17) 5.4营销地区 (18) 5.5营销策略 (18) 5.6媒介策略 (20)

1.执行概要 1.1企业简介 锐澳公司自成立起就致力于中国ALCOPOP酒（预调鸡尾酒）市场的开发与建立。2002年，源于在欧洲市场的成功经验，锐澳公司在英国联合了中国、英国和澳大利亚等科技人员在内的研发队伍，专门针对亚洲市场研发ALCOPOP酒（预调鸡尾酒），成功将原始配方研制完成。 2003年，在中国进行了8个月的市场测试后，适合中国消费群特点的ALCOPOP酒（低酒精饮品）“RIO”（锐澳）正式诞生，从而成为锐澳公司开拓中国ALCOPOP（预调鸡尾酒）市场的第一步。 2004年末，欧洲知名的BACCHUS公司在中国推出锐澳RIO，率先引进全新流行酒吧时尚概念――Alcopop（预调鸡尾酒)，四种纯正口味的RIO分别以WHSKY（威士忌）、VODKA（伏特加）、BRANDY（白兰地）、RUM（朗姆酒）为基酒，分别调入蓝玫瑰、香橙、水蜜桃、青柠健康果蔬汁，呈现蓝、橙、粉、青四种不同颜色，不仅口感纯正顺滑，而且赏心悦目，诱惑不同心情。预调酒：是由一种或多种烈性酒为基酒，与果汁、植物抽提物预先调配而成的酒，也可形象的称为“装在瓶中的鸡尾酒”或“预先调好的洋酒”。锐澳预调酒共有六种口味: 1、白兰地水蜜桃味：粉红色泽，法国干邑白兰地+纯鲜水蜜桃汁。2、朗姆青柠味：柠檬绿色泽，古巴朗姆酒+鲜柠榨汁。3、伏特加橙味：橙黄色泽，俄罗斯伏特加+鲜橙原汁，带来无限健康活力。4、威士忌蓝玫瑰味：色泽为诱惑蓝，苏格兰威士忌+天然玫瑰萃取精华。 5、伏特加宾治味：无色透明，俄罗斯伏特加+红石榴、凤梨、樱桃、芒果、西柚等多种果汁原汁。6、伏特加西菠柳味：淡橙色色泽，俄罗斯伏特加+西番莲、菠萝柳橙原汁。 2005年，经过短短一年的市场开拓，锐澳公司的RIO预调酒在中国许多区域已经成为预调酒领导品牌；同时，锐澳公司在中国跨出战略性一步，位于上海浦东的制造基地正式建成并投产，该基地是中国目前最大的预调酒制造基地。 随着世界范围经济和文化的交融发展，锐澳公司希望能联合中国成功的酒类经营企业，形成长期的战略合作伙伴关系，共同迎接中国市场ALCOPOP（预调鸡尾酒）流行时代的到来。 2007年，锐澳公司的产品即已覆盖全国市场。同时。RIO（锐澳）酒业深谙“质量是企业生命”的道理，严格执行质量管理体系，在业内率先通过ISO9001、ISO22000、HACCP等

rio鸡尾酒广告调研报告

河北建筑工程学院 题目：Rio鸡尾酒广告效应分析调研报告学院：经济管理学院 班级：房地产开发与管理131 成员：路雅霖2013329132 付曼2013329104 郭弋旋2013329103 李延枝2013329115 成绩：

目录 一调研背景 (2) 二调研目的 (2) 三调研对象 (3) 四调研内容 (3) 五调研方法 (3) 六调研时间 (3) 七调研结果及其分析 (3) 八广告优点与缺点分析 (5) 九调研附录 (8) 十详细资料附录 (10)

一、调研背景 伴随着“ I love RIO I love you ”这句广告词的流行，锐澳旗下的预调鸡尾酒横空出世。在广告宣传效益下，它不仅树立了自己的品牌，成为中国预调鸡尾酒市场的领先者，而且极大地拉动了销售量。RIO在中国多年专注于预调鸡尾酒行业，RIO正在不断引领市场格局。COCKTAIL传承了英国预调鸡尾酒的优质酿造工艺，并经过长期的研发，RIO瓶装、罐装鸡尾酒产品在中国市场中已占据半数市场。 但随着鸡尾酒市场的逐步成熟，其行内竞争也越来越激烈，所以为了进一步扩大产品销售量，更好地打开RIO鸡尾酒消费市场，拓展其市场发展空间，使其在激烈的竞争下立于不败之地，我们进行了一次RIO鸡尾酒广告效果的调研，希望通过分析调研结果，更好地进行广告营销决策。二、调研目的 本次市场调研须本着科学严谨、真实可靠、调查与论证相结合的原则，对此项调查进行深入的研究。本次市场调研的目的包括： 1、了解RIO鸡尾酒广告在不同消费者心中的整体形象，例如它的知名度、渗透率、美誉度和忠诚度。 2、明确消费者对RIO鸡尾酒的品牌认知。 3、掌握消费者得心理需要和购买能力。 4、分析消费者的购买动机及购买达成主要因素。 5、了解RIO鸡尾酒广告的优势和劣势。 6、掌握RIO鸡尾酒市场特征的消息和目前市场的现状。

RIO鸡尾酒调查报告

‘ 关于RIO鸡尾酒的调查报告 市营151 第五组 董依甜39刘滢婷32魏凯东 43 吴慧慧17周倩妃36 陈正立19应慈阳50

目录 摘要 (3) 引言 (3) 一、调查概况 (3) 二、调查对象的基本特征 (3) 三、消费者XX产品消费行为分析 (4) （一）消费者对农产品质量安全信息的关注程度 (5) （二）消费者对各种信息提供渠道的认可程度 (5) （三）消费者对农产品质量安全状况的评价..................................... 错误！未定义书签。 （四）消费者对农产品标注信息的关注............................................. 错误！未定义书签。 （五）消费者对认证农产品的关注及认可程度................................. 错误！未定义书签。 （六）消费者对政府和农户提高农产品质量安全水平的认知度..... 错误！未定义书签。 四、影响消费者农产品质量安全意识的因素分析（交叉分析） (6) （一）消费者基本特征 (6) （二）消费者购买安全农产品的经验 (6) （三）安全农产品与常规农产品价格差异的大小............................. 错误！未定义书签。 五、结论与建议 (6) （一）结论 (6) （二）建议 (6)

摘要 本调查报告在对XX省XX市9县（市）消费者调查的基础上，对消费者的农产品质量安全意识状况和消费者消费行为的影响因素进行了调查分析。研究结果显示，广大消费者对农产品质量安全非常关注，消费者比较相信政府提供的农产品质量安全信息，但他们对目前的农产品质量安全状况的评价不容乐观。消费者对农产品质量安全的认知水平主要与消费者性别、受教育程度、家庭规模等因素有关。消费者对认证食用农产品的认知水平和消费欲望比较高。此外，政府目前最需要的是对虚假标注农产品质量安全信息的监管。 引言 近年来，RIO鸡尾酒作为一种混合饮品，成为消费者关注的热点。作为一种混合饮品，其最大特点是由两种或两种以上的酒或饮料、果汁、汽水混合而成，最后还可以用柠檬片、水果或薄荷叶作为装饰物。有一定的营养价值和欣赏价值。它不仅能让神经缓和还能让肌肉放松 一、调查概况 本调查报告所用数据来源于2016年3-4月的问卷调查。在充分考虑样本的分散性和随机性的基础上，本次调查的地点主要集中在下沙高教园东区的数所高校中，调查对象分为不同的性别、年龄、职业的消费者。问卷中关于是否饮用过RIO鸡尾酒,购买频率,性别,年级等为单项选择题，关于影响购买的因素为多项选择题，在问卷调查过程中，共发放问卷120份，回收108份，回收率为90%，经过对回收调查问卷的审核，得到有效问卷104份。 二、调查对象的基本特征 从被调查者的性别来看，男性占46.2%，女性占53.8%；从年级构成来看，大一学生占绝大多数(64.4%)，这表明大一学生是消费最旺盛的群体，同时也表明本调查报告中分析的购买RIO鸡尾酒的人群比较接近青年人的水平。