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In intra video coding and image coding, the directional intra prediction is used to reduce spatial redundancy. Intra prediction residuals are encoded with transforms. In this paper, we develop transforms for directional intra prediction residuals. Specifically, we observe that the directional intra prediction is most effective in smooth regions and edges with a particular direction. In the ideal case, edges can be predicted fairly accurately with an accurate prediction direction. In practice, an accurate prediction direction is hard to obtain. Based on the inaccuracy of prediction direction that arises in the design of many practical video coding systems, we can estimate the residual variance and propose a class of transforms based on the estimated variance function. The proposed method is evaluated by the energy compaction property. Experimental results show that with the proposed method, the same amount of energy in directional intra prediction residuals can be preserved with a significantly smaller number of transform coefficients.

In the Differential Pulse-code Modulation (DPCM) image coding, the intensity of a pixel is predicted as a linear combination of a set of surrounding pixels and the prediction error is encoded. In this paper, we propose the adaptive residual DPCM (ARDPCM) for intra lossless coding. In the ARDPCM, intra residual samples are predicted using adaptive mode-dependent DPCM weights. The weights are estimated by minimizing the Mean Squared Error (MSE) of coded data and they are synchronized at the encoder and the decoder. The proposed method is implemented on the High Efficiency Video Coding (HEVC) reference software. Experimental results show that the ARDPCM significantly outperforms HEVC lossless coding and HEVC with the DPCM. The proposed method is also computationally efficient.

In this paper, we present a novel method for inverse filtering a two dimensional (2-D) signal using phase-based processing techniques. A 2-D sequence can be represented by a sufficient number of samples of the phase of its Fourier transform and its region of support. This is exploited to perform deconvolution. We examine the effects of additive noise and incomplete knowledge of the point spread function on the performance of this deconvolution method and compare it with other 2-D deconvolution methods. The problem of finding the region of support will also be briefly addressed. Finally, an application example will be presented.

Transforms used in image coding are also commonly used to compress prediction residuals in video coding. Prediction residuals have different spatial characteristics from images, and it is useful to develop transforms that are adapted to prediction residuals. In this paper, we explore the differences between the characteristics of images and motion compensated prediction residuals by analyzing their local anisotropic characteristics and develop transforms adapted to the local anisotropic characteristics of these residuals. The analysis indicates that many regions of motion compensated prediction residuals have 1-D anisotropic characteristics and we propose to use 1-D directional transforms for these regions. We present experimental results with one example set of such transforms within the H.264/AVC codec and the results indicate that the proposed transforms can improve the compression efficiency of motion compensated prediction residuals over conventional transforms.

Typically the same transforms, such as the 2-D Discrete Cosine Transform (DCT), are used to compress both images in image compression and prediction residuals in video compression. However, these two signals have different spatial characteristics. In, we analyzed the difference between these two signals and proposed 1-D directional transforms for prediction residuals. In this paper, we provide further experimental results using these transforms in the H.264/AVC codec and present other related information which can provide insights in understanding the use of these transforms in video coding applications.

When interlaced scan (IS) is used for television transmission, the received video must be deinterlaced to be displayed on progressive scan (PS) displays. To achieve good performance, the deinterlacing operation is typically computationally expensive. We propose a receiver compatible approach which performs a deinterlacing operation inexpensively, with good performance. At the transmitter, the system analyzes the video and transmits an additional low bit-rate stream. Existing receivers ignore this information. New receivers utilize this stream and perform a deinterlacing operation inexpensively with good performance. Results indicate that this approach can improve the digital television standard in a receiver compatible manner.

Multiple description (MD) video coding can be used to reduce the detrimental effects caused by transmission over lossy packet networks. A number of approaches have been proposed for MD coding, where each provides a different tradeoff between compression efficiency and error resilience. How effectively each method achieves this tradeoff depends on the network conditions as well as on the characteristics of the video itself. This paper proposes an adaptive MD coding approach which adapts to these conditions through the use of adaptive MD mode selection. The encoder in this system is able to accurately estimate the expected end-to-end distortion, accounting for both compression and packet loss-induced distortions, as well as for the bursty nature of channel losses and the effective use of multiple transmission paths. With this model of the expected end-to-end distortion, the encoder selects between MD coding modes in a rate-distortion (R-D) optimized manner to most effectively tradeoff compression efficiency for error resilience. We show how this approach adapts to both the local characteristics of the video and network conditions and demonstrates the resulting gains in performance using an H.264-based adaptive MD video coder.

Contains an introduction and reports on five research projects., National Science Foundation Fellowship, National Science Foundation Grant MIP 87-14969, U.S. Navy - Office of Naval Research Contract N00014-89-J-1489, U.S. Air Force - Electronic Systems Division Contract F19628-89-K-0041

Contains research objectives and summary of research on five research projects, with ten sub-topics., National Institutes of Health (Grant 1 RO1 NS10916-01), National Institutes of Health (Grant 5 RO1 NS11000-03), National Institutes of Health (Grant 1 RO1 NS11153-01), Harvard-M.I.T. Rehabilitation Engineering Center, U. S. Department of Health, Education, and Welfare (Grant 23-P-55854), National Institutes of Health (Grant 1 RO1 NS11680-01), National Institutes of Health (Grant 5 ROI NS11080-02), M.I.T. Health Sciences Fund, National Aeronautics and Space Administration (Grant NSG-2032), National Institutes of Health (Grant 5 TO1 GM01555-09), Massachusetts General Hospital Purchase Order F63853, Boston City Hospital Purchase Order 4338-75431

Various directional transforms have been developed recently to improve image compression. In video compression, however, prediction residuals of image intensities, such as the motion compensation residual or the resolution enhancement residual, are transformed. The applicability of the directional transforms on prediction residuals have not been carefully investigated. In this paper, we briefly discuss differing characteristics of prediction residuals and images, and propose directional transforms specifically designed for prediction residuals. We compare these transforms with the directional transforms proposed for images using prediction residuals. The results of the comparison indicate that our proposed directional transforms can provide better compression of prediction residuals than the directional transforms proposed for images.

Contains table of contents for Part III, table of contents for Section 1, an introduction and reports on seventeen research projects., National Science Foundation Fellowship, National Science Foundation (Grant ECS 84-07285), National Science Foundation (Grant MIP 87-14969), U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742), Scholarship from the Federative Republic of Brazil, U.S. Air Force - Electronic Systems Division (Contract F19628-85-K-0028), AT&T Bell Laboratories Doctoral Support Program, Canada, Bell Northern Research Scholarship, Canada, Fonds pour la Formation de Chercheurs et I'Aide a la Recherche Postgraduate Fellowship, Sanders Associates, Inc., OKI Semiconductor, Inc., Tel Aviv University, Department of Electronic Systems, U.S. Navy - Office of Naval Research (Contract N00014-85-K-0272), Natural Sciences and Engineering Research Council of Canada, Science and Engineering Scholarship

Contains a research summary and reports on fifteen research projects., National Science Foundation Fellowship, Joint Services Electronics Program (Contract DAAG29-78-C-0020), National Science Foundation (Grant ENG76-24117), U.S. Navy - Office of Naval Research (Contract N00014-75-C-0951), Schlumberger-Doll Research Center Fellowship, Hertz Foundation Fellowship, National Aeronautics and Space Administration (Grant NSG-5157), U.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)

The Discrete-Cosine-Transform (DCT) is the most widely used transform in image and video compression. Its use in image compression is often justified by the notion that it is the statistically optimal transform for first-order Markov signals, which have been used to model images. In standard video codecs, the motion-compensation residual (MC-residual) is also compressed with the DCT. The MC-residual may, however, possess different characteristics from an image. Hence, the question that arises is if other transforms can be developed that can perform better on the MC-residual than the DCT. Inspired by recent research on direction-adaptive image transforms, we provide an adaptive auto-covariance characterization for the MC-residual that shows some statistical differences between the MC-residual and the image. Based on this characterization, we propose a set of block transforms. Experimental results indicate that these transforms can improve the compression efficiency of the MC-residual.