Determining optical flow

We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.

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The Laplacian Pyramid as a Compact Image Code

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

A unified approach to the coder control of video coding standards such as MPEG-2, H.263, MPEG-4, and the draft video coding standard H.264/AVC (advanced video coding) is presented. The performance of the various standards is compared by means of PSNR and subjective testing results. The results indicate that H.264/AVC compliant encoders typically achieve essentially the same reproduction quality as encoders that are compliant with the previous standards while typically requiring 60% or less of the bit rate.

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Rate-constrained coder control and comparison of video coding standards

The relative efficiency of any particular image-coding scheme should be defined only in relation to the class of images that the code is likely to encounter. To understand the representation of images by the mammalian visual system, it might therefore be useful to consider the statistics of images from the natural environment (i.e., images with trees, rocks, bushes, etc). In this study, various coding schemes are compared in relation to how they represent the information in such natural images. The coefficients of such codes are represented by arrays of mechanisms that respond to local regions of space, spatial frequency, and orientation (Gabor-like transforms). For many classes of image, such codes will not be an efficient means of representing information. However, the results obtained with six natural images suggest that the orientation and the spatial-frequency tuning of mammalian simple cells are well suited for coding the information in such images if the goal of the code is to convert higher-order redundancy (e.g., correlation between the intensities of neighboring pixels) into first-order redundancy (i.e., the response distribution of the coefficients). Such coding produces a relatively high signal-to-noise ratio and permits information to be transmitted with only a subset of the total number of cells. These results support Barlow's theory that the goal of natural vision is to represent the information in the natural environment with minimal redundancy.

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Relations between the statistics of natural images and the response properties of cortical cells.

From the Publisher:
This book offers comprehensive coverage of the MPEG-2 audio / visual digital compression standard. The treatment includes the specifics needed to implement an MPEG-2 Decoder, including the syntax and semantics of the coded bitstreams. Since the MPEG-2 Encoders are not specified by the standard, and are actually closely held secrets of many vendors, the book only outlines the fundamentals of encoder design and algorithm optimization.

Digital Video: An introduction to MPEG-2

New updated! The latest book from a very famous author finally comes out. Book of digital pictures representation and compression, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

Digital Pictures: Representation and Compression

We present methods of estimating displacements of moving objects from one frame to the next in a television scene and using such displacements for frame-to-frame prediction. Displacement is estimated by a recursive algorithm which seeks to minimize a functional of the prediction error. Several simplifications of the algorithm are presented which make it attractive for hardware implementation. Performance of the algorithm is evaluated by computer simulations on two sequences of moving images containing various amounts and types of motion. In both cases, the use of displacement-based (or motion-compensated) prediction results in bit rates that are 22 to 50 percent lower than those obtained by simple “frame-difference” prediction, which is used commonly in the interframe coders.

Motion-compensated television coding: Part I

This paper presents a review of techniques used for digital encoding of picture material. Statistical models of picture signals and elements of psychophysics relevant to picture coding are covered first, followed by a description of the coding techniques. Detailed examples of three typical systems, which combine some of the coding principles, are given. A bright future for new systems is forecasted based on emerging new concepts, technology of integrated circuits and the need to digitize in a variety of contexts.

Picture coding: A review

Problems of signal processing arise in image synthesis because of transformations between continuous and discrete representations of 2D images. Aliasing introduced by sampling has received much attention in graphics, but reconstruction of samples into a continuous representation can also cause aliasing as well as other defects in image quality. The problem of designing a filter for use on images is discussed, and a new family of piecewise cubic filters are investigated as a practical demonstration. Two interesting cubic filters are found, one having good antialiasing properties and the other having good image-quality properties. It is also shown that reconstruction using derivative as well as amplitude values can greatly reduce aliasing.

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Arun N. Netravali

Papers

Digital Video: An introduction to MPEG-2

Digital Pictures: Representation and Compression

Motion-compensated television coding: Part I

Picture coding: A review

Reconstruction filters in computer-graphics