Multiresolution vector quantization

Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes

A high-speed codebook design algorithm for ECVQ using angular constraint with search space partitioning

金沢大学大学院自然科学研究科情報システム金沢大学工学部In this paper, we propose a fast codebook generation algorithm for entropy-constrained vector quantization (ECVQ). The algorithm uses the angular constraint and employs a suitable hyperplane to partition the codebook and image data in order to reduce the search area and accelerate the search process in the codebook design. This algorithm allows significant acceleration in codebook design process. Experimental results are presented on image block data. These results show that our new algorithm performs better than the previously known methods

Fast codeword search algorithm for ECVQ using hyperplane decision rule

金沢大学大学院自然科学研究科情報システム金沢大学工学部Vector quantization is the process of encoding vector data as an index to a dictionary or codebook of representative vectors. One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through the codebook. Entropy-constrained vector quantization (ECVQ) codebook design based on empirical data involves an expensive training phase in which Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we describe a new method allowing significant acceleration in codebook design process. This method has feature of using a suitable hyperplane to partition the codebook and image data. Experimental results are presented on image block data. These results show that our method performs better than previously known methods

Fast search algorithms for ECVQ using projection pyramids and variance of codewords

金沢大学大学院自然科学研究科情報システム金沢大学工学部Vector quantization for image compression requires expensive time to find the closest codeword through the codebook. Codebook design based on empirical data for entropy-constrained vector quantization (ECVQ) involves a time consuming training phase in which a Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we propose two fast codebook generation methods for ECVQ. In the first one, we use an appropriate topological structure of input vectors and codewords to reject many codewords that are impossible to be candidates for the best codeword. In the second method, we use the variance test to increase the ability of the first algorithm to reject more codewords. These algorithms allow significant acceleration in the codebook design process. Experimental results are presented on image block data. These results show that our new algorithms perform better than the previously known methods

Scalable video compression with optimized visual performance and random accessibility

This thesis is concerned with maximizing the coding efficiency, random accessibility and visual performance of scalable compressed video. The unifying theme behind this work is the use of finely embedded localized coding structures, which govern the extent to which these goals may be jointly achieved. The first part focuses on scalable volumetric image compression. We investigate 3D transform and coding techniques which exploit inter-slice statistical redundancies without compromising slice accessibility. Our study shows that the motion-compensated temporal discrete wavelet transform (MC-TDWT) practically achieves an upper bound to the compression efficiency of slice transforms. From a video coding perspective, we find that most of the coding gain is attributed to offsetting the learning penalty in adaptive arithmetic coding through 3D code-block extension, rather than inter-frame context modelling. The second aspect of this thesis examines random accessibility. Accessibility refers to the ease with which a region of interest is accessed (subband samples needed for reconstruction are retrieved) from a compressed video bitstream, subject to spatiotemporal code-block constraints. We investigate the fundamental implications of motion compensation for random access efficiency and the compression performance of scalable interactive video. We demonstrate that inclusion of motion compensation operators within the lifting steps of a temporal subband transform incurs a random access penalty which depends on the characteristics of the motion field. The final aspect of this thesis aims to minimize the perceptual impact of visible distortion in scalable reconstructed video. We present a visual optimization strategy based on distortion scaling which raises the distortion-length slope of perceptually significant samples. This alters the codestream embedding order during post-compression rate-distortion optimization, thus allowing visually sensitive sites to be encoded with higher fidelity at a given bit-rate. For visual sensitivity analysis, we propose a contrast perception model that incorporates an adaptive masking slope. This versatile feature provides a context which models perceptual significance. It enables scene structures that otherwise suffer significant degradation to be preserved at lower bit-rates. The novelty in our approach derives from a set of "perceptual mappings" which account for quantization noise shaping effects induced by motion-compensated temporal synthesis. The proposed technique reduces wavelet compression artefacts and improves the perceptual quality of video