资源目录
压缩包内文档预览：
编号：511944
类型：共享资源
大小：1.49MB
格式：RAR
上传时间：2015-11-11
上传人：QQ28****1120
认证信息
个人认证
孙**（实名认证）
辽宁
IP属地：辽宁
6
积分
- 关 键 词：
-
毕业设计
- 资源描述：
-
39.静止图像压缩技术JPEG的分析与应用,毕业设计
- 内容简介：
-
1/8 1. Introduction and Objectives 1.1 Introduction Visual information is of vital importance if human beings are to perceive, recognize and understand the surrounding world. With the tremendous progress that has been made in computer power, the corresponding growth in the multimedia market and the advent of the World Wide Web, it is becoming increasingly possible for image to be widely utilized in our daily life. In general, an image file contains much more data than a text file. An image with large amount of data requires more memory to store, takes longer to transfer, and is difficult to process. For example, a black and white image with 256 256 pixels requests about 64 Kilobytes of memory space and more than 6 seconds to download using a Dialup Internet connection. As a consequence, image compression becomes necessary due to the limited communication bandwidth, CPU (for computer Central Processing Unit) speed and storage size. Image compression has been pushed to the forefront in the image processing field. Fractal image compression is a relatively recent image compression method which exploits similarities in different parts of the image. For example, with a picture of a fern (Figure 1.) one can see easily where these similarities lie: each fern leaf resembles a smaller fern. This is known as the famous Barnsley fern 1. During more than two decades of development, the IFS (Iterated Function System) based compression algorithm stands out as the most promising direction for further research and improvement 2. The basic idea is to represent an image by a union of fractals, of which each is the fixed point of an IFS. Therefore, an input image can be approximated by a series of IFS codes. In this way, an amazing compression ratio can be achieved 3. Figure 1. A fern leaf The implementation of the fractal-based image compression method to be researched in my PhD study is based on a novel image structure, the Spiral Architecture 4, which is inspired from anatomical considerations of the primates vision 5. On the Spiral nts2/8 Architecture, an image is a collection of hexagonal elements 6. Each pixel on the Spiral Architecture is identified by a designated positive number, called Spiral Address as shown in Figure 2. The numbered hexagons form the cluster of size 7n and these hexagons tile the plane in a recursive modular manner along the Spiral Direction 7 so that the Spiral Architecture (SA) is constructed. Figure 2. A collection of 72 = 49 Hexagons with labeled addresses In our preliminary research on image compression based on the Spiral Architecture, we have observed that compared with rectangular structure the Spiral Architecture has two superior characteristics to improve image compression. The first, called Locality of Pixel Density 8, refers to the number of changes in pixel values within a certain length of a run, that is, a certain number of pixels. The difference of light intensities between pixels is highly related to the physical distance between them. The closer they are, the smaller the intensity difference will be 9. It has been proven that the Spiral Architecture could introduce smaller differences between neighboring pixels when saving image data as a one-dimensional data set. Thus, SA may have the potential for better compression on all images. Another characteristic of interest is called Uniform Image Partitioning 10, by which the input image could be partitioned into a few similar sub-images using Spiral Multiplication as shown in 7. No matter how many sub-images are generated as the result of Spiral Multiplication, each of them is a scaled-down approximate copy of the original image and very similar to each other. Therefore this may introduce an opportunity to easily and quickly detect the self-similarity in an image so that it would reduce the computational complexity in the implementation of fractal image compression algorithm. 1.2 Aims and Objectives The research work to be done in my PhD study is the preliminary work towards a future goal: to develop a novel JPEG-like image compression format (or standard) on SA that will eventually compete with JPEG and other popular compression standards. The algorithms to be developed will be based on the recently developed technique fractal image nts3/8 compression. It will be the first time to see this extremely powerful technique working for image compression on the SA that the Computer Vision Research Group (in Faculty of IT at University of Technology, Sydney) have been working on since 1996. The proposed algorithms will first be implemented on grey-level images and then converted to color images and videos. The objectives of my PhD study are: 1. to investigate features of SA for fractal image compression, 2. to develop compression algorithms based on SA that will be proven to be better than existing fractal-based algorithms in terms of compression ratio and accuracy, 3. to optimize the proposed fractal image compression algorithms on SA by introducing novel partitioning and searching methods, domain pool classification, parallel processing and so on, and 4. to analyze the results of the fractal compression algorithms on SA statistically and compare with other popular image compression techniques to prove its better performance. 2 Background (Including Significance and Novel Elements) 2.1 Concepts of Fractal Image Compression In the following section, the basic concepts of fractal image compression on the traditional square structure would be introduced. Before delving into details, there are some highlights of fractal image compression. 1. It is a promising technology, though still relatively immature. 2. The fractals are represented by Iterated Function Systems (IFSs). 3. It is a block-based lossy compression method. 4. Compression has traditionally been slow but decompression is fast. 2.1.1Theory and Mathematics Background The fundamental principle of fractal image compression is to represent an image by iterated function systems (IFSs), of which each fixed point is collaged together to approximate that image. Each fixed point is then a fractal 11. Those IFSs will be coded as contractive transformations with coefficients so that the given image is encoded accordingly. Banachs fixed point theorem guarantees that, within a complete metric space, the fixed point of such a transformation may be recovered by iterated implementation thereof to an arbitrary initial element of that space 12. Therefore, the encoding process of fractal image compression is to find an IFS whose fixed point is close to the given image. The usual approach is based on the Collage Theorem, which provides a bound on the difference between the image to be encoded and the fixed point of an IFS (more details please refer to 11 chapter 2). A suitable transformation may therefore be constructed as a nts4/8 collage from the image to itself with a sufficiently small collage error (the distance between the collage and the image) guaranteeing that the fixed point of that transformation is close to the original image 13. In the original approach, devised by Barnsley, this transformation was composed of the union of a number of affine mappings on the entire image 14. While a few impressive examples of image modeling were generated by this method (Barnsleys fern, for example 15), no automated encoding algorithm was found. Fractal image compression became a practical reality with the introduction by Jacquin of the partitioned IFS (PIFS) 16, which differs from an IFS in that each individual transformation operates on a subset of the image, rather than the entire image. Since the image is tiled by range blocks, each of which is mapped from one of the domain blocks as depicted in Figure 3, the combined mappings constitute a transformation on the image as a whole. The transformation minimizing the collage error within this framework is constructed by individually minimizing the collage error for each range block, which requires locating the domain block which may be made closest to it under an admissible block mapping. This transformation is then represented by specifying, for each range block, the identity of the matching domain block together with the block mapping parameters minimizing the collage error for that range block. Figure3. Each range block is constructed by a transformed domain block 2.1.2Summary and Significance of Fractal Image Compression Fractal image compression as proposed here reduces the redundancy of images by using the self-similarity in images, that is, one part of an image can always be found similar to another part of the image 17. So an original image is first partitioned into non-overlapped range blocks and the best-matched domain block is searched for each of these range blocks. The relationship between a range block and its best match is represented by an IFS. In this way, the image is saved as codes of IFSs eventually. Therefore, this method can potentially achieve an amazing high compression ratio of 10000:1 3. Another advantage of fractal image compression is its fast decoding/decompressing process. A fractal-based compressed image file has a set of codes for IFSs and it requires only simple computation to obtain the fixed point of each IFS and to recover the original image. Thus, this method is suitable for the applications, in which one compressed image is decompressed by many users but the time for compression process is trivial and unimportant. These are the nts5/8 motivations to concentrate on the fractal image compression in my PhD study. 2.2 Spiral Architecture 2.2.1 Hexagonal Structure for Image Processing The possibility of using a hexagonal grid to represent digital images has been studied for more than thirty years. Hexagonal girds have a higher degree of symmetry than the rectangular grids. This symmetry results in a considerable savings of both storage and computation time 18, 19. On hexagonal architecture, each grid has only six neighboring grids which have the same distance to the center. It is a different grid arrangement scheme from the rectangular architecture which uses a set of 3 3 vision unit (see Figure 4). Because of the uniformly connected and close-packed form along, greater angular resolution, higher efficiency and better performance are achieved in many hexagonal systems 18-20. Figure 4. Vision Unit in Two Different Image Architectures In order to widely utilize the hexagonal grid system on image processing area, many mathematic foundations have been specially developed for hexagonal grid. Her 20 proposed a symmetrical hexagonal coordinate frame to identify each grid on hexagonal architecture. According to symmetrical hexagonal coordinate frame, each grid point uses integer coordinates with three components, which describe the hexagonal grid on a virtual three-dimension space. This method makes the hexagonal system compatible with digital computation. However, there is one more coordinate component introduced in Hers coordinate system than traditional rectangular architecture with two components and that will increase computational complexity in some degree. Moreover, Her still borrowed mathematics from the rectangular system to apply his hexagonal coordinates frame on image transformation, so it is inevitable to perform floating metric computation 21. Both of the two disadvantages make Hers solution imperfect to illuminate the superior characteristics carried by hexagonal image structure. Fortunately, to solve Hers problems Sheridan 22 proposed another single component hexagonal addressing system called Spiral Addressing and defined the hexagonal grid system with his spiral addressing scheme as the Spiral Architecture (SA). (a) Rectangular Architecture (b) Hexagonal Architecture nts6/8 2.2.2 Spiral Addressing The first step in SA formulation is to label all the hexagons with a unique address. The addresses of these hexagons will be the only component needed to refer the hexagons. The addressing process is achieved by initially labeling seven-based integer addresses to a collection of seven hexagons, namely 0, 1, 2, 3, 4, 5 and 6 as displayed in Figure 5. Figure 5. A collection of seven hexagons with unique addresses Then we could easily define the six neighboring collections of seven hexagons around the first one. For each neighboring collection of seven hexagons, label hexagons through spiral direction consecutively from the centre using 7-based integers as we did for the first seven hexagons (see Figure 6). The repetition of the above steps will cover the whole image and eventually generate the SA. This addressing method permits the collection of hexagons to grow in power of seven with uniquely assigned addresses and those addresses are consecutive in base seven. It is the foundation of the two algebraic operations defined on Spiral Architecture. 2.2.3 Algebraic Operations on Spiral Architecture 2 3 0 4 5 6 1 Figure 6. Spiral Architecture with the First 49 Hexagons 4 5 6 1 3 2 0 44 45 46 41 43 42 40 54 55 56 51 53 52 50 64 65 66 61 63 62 60 14 15 16 11 13 126 10 24 25 26 21 23 22 20 34 35 36 31 33 32 30 Main spiral direction Secondary spiral direction nts7/8 As described above, SA contains very useful geometric and algebraic properties, which can be interpreted in terms of a mathematical object, the Euclidean ring. Two algebraic operations have been defined on SA: Spiral Addition and Spiral Multiplication, both of which share the same basic algorithm Spiral Counting. Spiral Counting can be considered as a spiral movement that given a commencing hexagon, counts for a pre-determined number and terminates at another certain hexagon. Any hexagon in an image can be reached by Spiral Counting from any other given hexagon in the same image. When applying Spiral Counting, it is strictly dependent on a pre-determined key define by Sheridan in 4. A key is used to represent the spatial relationship between the given hexagon and the hexagon with spiral address zero. It determines two important parameters: the distance and the orientation. For instance, given a Spiral address 15, the key of 15 can determine two values. One is the distance between the given hexagon 15 to the hexagon 0; the other is the orientation of hexagon 15 from hexagon 0. We could use the angle to represent the orientation (Figure 7). Figure 7. The key of hexagon 15 2.2.4 Summary of the Advantages Introduced by SA In summary, while applying SA in image processing, Spiral Addition and Spiral Multiplication are very useful and powerful tools. Both of them are computationally simple and of high speed 7. Furthermore, the first is essential to locate a hexagonal pixel in an image and also defines the image translation on SA. The latter can provide a novel image-partitioning scheme, namely Uniform Partitioning, with which the given image can be separated into a certain number of sub-images and these sub-images are all scaled-down copies of the given image with subtle difference. Combining the two operations together, we have noticed that SA will bring a breakthrough into fractal image compression by introducing novel schemes in defining range blocks (image partitioning), block searching (self-similarity detecting), distributed image processing and so on. Those potential schemes will be further discussed in Section 4. Distance: Orientation: nts8/8 2.3 Virtual Spiral Architecture As discussed earlier, SA has two unbeatable characters that are expected to improve image compression performance: Locality of Pixel Intensity and Uniform Image Partitioning 8. However due to the lack of capture and display devices, SA has not yet been widely us
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
川公网安备: 51019002004831号