Jacques Vaisey

Image compression with variable block size segmentation

High-quality variable-rate image compression is achieved by segmenting an image into regions of different sizes, classifying each region into one of several perceptually distinct categories, and using a distinct coding procedure for each category. Segmentation is performed with a quadtree data structure by isolating the perceptually more important areas of the image into small regions and separately identifying larger random texture blocks. Since the important regions have been isolated, the remaining parts of the image can be coded at a lower rate than would be otherwise possible. High-quality coding results are achieved at rates between 0.35 and 0.7 b/p depending on the nature of the original image, and satisfactory results have been obtained at 0.25 b/p. >

On the joint source-channel decoding of variable-length encoded sources: the BSC case

This paper proposes an optimal maximum a posteriori probability decoder for variable-length encoded sources over binary symmetric channels (BSC) that uses a novel state-space to deal with the problem of variable-length source codes in the decoder. This sequential, finite-delay, joint source-channel decoder delivers substantial improvements over the conventional decoder and also over a system that uses a standard forward error correcting code operating at the same over all bit rates. This decoder is also robust to inaccuracies in the estimation of channel statistics.

Low entropy image pyramids for efficient lossless coding

An efficient image source coding technique gives good compression performance at low computational complexity. This research introduces an efficient coding technique, based on pyramid coding, that involves transforming an image into an equivalent lower entropy form prior to lossless coding. The proposed method is also a multiresolution technique that facilitates progressive image transmission.

Improving motion compensation using multiple temporal frames

The authors describe a novel algorithm for block-based motion compensation that reduces both the energy in the displaced frame difference signal and the computational complexity compared to traditional methods. The new technique consists of changing the search area so that it includes regions from several temporal frames. Results show that the drop in energy can be quite dramatic (up to a factor of two) in some cases.<<ETX>>

On the joint source-channel decoding of variable-length encoded sources: the additive-Markov case

We propose an optimal joint source-channel maximum a posteriori probability decoder for variable-length encoded sources transmitted over a wireless channel, modeled as an additive-Markov channel. The state space introduced by the authors in a previous paper is used to take care of the unique challenges posed by variable-length codes. Simulations demonstrate, that this decoder performs substantially better than the standard Huffman decoder for a simple test source and is robust to inaccuracies in channel statistics estimates. The proposed algorithm also compares favorably to a standard forward error correction-based system.

Bayesian motion estimation and interpolation in interlaced video sequences

Motion estimation in video is a very important tool for such applications as interpolation, motion compensation, and segmentation. However, in interlaced video, it is often desirable to perform interpolation prior to the motion estimation. In this correspondence, a novel technique is presented that, rather than minimizing the interpolation error in each of the frames used for motion estimation, seeks to make these errors equal. The new method is shown to perform deinterlacing better than several other approaches also based on Bayesian estimation.

Joint source-channel decoding of entropy coded Markov sources over binary symmetric channels

This paper proposes an optimal joint source-channel, maximum a posteriori, decoder for entropy coded Markov sources transmitted over noisy channels. We introduce the concept of incomplete and complete states to deal with the problem of variable length source codes in the decoder. The proposed decoder is sequential, thereby making the expected delay finite. When compared to the traditional decoder, the proposed decoder shows a maximum improvement, of about 4 dB in a modified signal to noise ratio and an improvement of 21.95% in percentage of bits that are received in an out of synchronization condition, for a simple test source.

Optimal decoding of entropy coded memoryless sources over binary symmetric channels

Summary form only given. Entropy codes (e.g. Huffman codes) are often used to improve the rate-distortion performance of codecs for most sources. However, transmitting entropy coded sources over noisy channels can cause the encoder and decoder to lose synchronization, because the codes tend to be of variable length. Designing optimal decoders to deal with this problem is nontrivial since it is no longer optimal to process the data in fixed-length blocks, as is done with fixed-length codes. This paper deals with the design of an optimal decoder (MAPD), in the maximum a posteriori (MAP) sense, for an entropy coded memoryless source transmitted over a binary symmetric channel (BSC) with channel cross over probability /spl epsiv/. The MAP problem is cast in a dynamic programming framework and a Viterbi like implementation of the decoder is presented. At each stage the MAPD performs two operations: the metric-update and the merger-check operations. A stream of 40,000 samples of a zero mean, unit variance, Gaussian source, quantized with uniform, N-level quantizers was Huffman encoded and the resulting bit stream was transmitted over a BSC. Experiments were performed for values of N ranging from 128 to 1024 and for four different random error patterns, obtained using a random number generator. The results demonstrate that the MAPD performs better than the HD on an average, whenever /spl epsiv/ is comparable to the source probabilities. A maximum reduction of 2.94% in the bits that are out of synchronization, was achieved for the 1024 level quantizer.

Multispectral image coding using lattice VQ and the wavelet transform

This paper examines the problem of compressing multispectral images using the wavelet transform and stack-run entropy coding. Our goal is to explore various ways of coding the wavelet coefficients in order to see which techniques can best exploit the correlation between the multispectral bands to produce an efficient coding algorithm. The results of our study indicate that applying the KLT to each subband followed by lattice VQ on Z/sup n/ and subsequent independent entropy coding of each of the lattice dimensions is an effective and fairly simple coding technique. We also demonstrate the importance of proper bit-allocation or, equivalently, the correct choice of the lattice scale parameter for each subband.

Subband prediction using leakage information in image coding

This paper examines the potential uses of interband leakage in image coders based on subband decomposition. The approach taken is to use the lowpass subband to predict the signal behavior in the other bands. This information is then used to both inform the coder/decoder as to likely locations for significant energy in the highpass bands, and to form a difference signal that can be coded at a lower rate. These methods give significant gains for some image/filter combinations, but in general it seems that analysis filters producing low correlation should be used if possible. >