The research described in this thesis addresses aspects of coding of discrete-cosinetransform (DCT) coefficients, that are present in a variety of transform-based digital-image-compression schemes such as JPEG. Coefficient reordering; that directly affects the symbol statistics for entropy coding, and therefore the effectiveness of entropy coding; is investigated. Adaptive zigzag reordering, a novel versatile technique that achieves efficient reordering by processing variable-size rectangular sub-blocks of coefficients, is developed. Classification of blocks of DCT coefficients using an artificial neural network (ANN) prior to adaptive zigzag reordering is also considered.

Some established digital-image-compression techniques are reviewed, and the JPEG standard for the DCT-based method is studied in more detail. An introduction to artificial neural networks is provided. 

Lossless conversion of blocks of coefficients using adaptive zigzag reordering is investigated, and experimental results are presented. A versatile algorithm, that generates zigzag scan paths for sub-blocks of any dimensions using a binary decision tree, is developed. An implementation of the algorithm based on programmable logic devices (PLDs) is described demonstrating the feasibility of hardware implementations. Coding of the sub-block dimensions, that need to be retained in order to reconstruct a sub-block during decoding, based on the scan-path length is developed.

Lossy conversion of blocks of coefficients is also considered, and experimental results are presented. A two-layer feedforward artificial neural network trained using an error-backpropagation algorithm, that determines the sub-block dimensions, is described. Isolated nonzero coefficients of small significance are discarded in some blocks, and therefore smaller sub-blocks are generated.

JPEG-like Image Compression using Neural-network-based Block Classification and Adaptive Reordering of Transform Coefficients

Vector quantization (Vq) provides us with an appropriate technique to obtain high compression ratios. However, for image sequence coding, vector quantizer has to design temporally stable codebooks of representative vectors and this constraint of temporal stability has to be solved with a minimal computational complexity. We propose a new coding scheme based on visual classification and temporal refreshment procedures which enable to design subjectively optimal codebooks for any kind of real broadcast image sequence. This contribution reports results of simulations about how such a classification may be introduced and evaluated and how vector quantizer may be refreshed. We obtain significantly better reconstruction quality for a given codebook size and investigate several available solutions to implement the codebook refreshment procedures.

Temporal Adaptive Vector Quantization For Image Sequence Coding

An adaptive image coding system is presented, in which vector auantization (VO) is implemented in the discrete cosine transformed (DCT) domain to manipulate the stationary image data according to their features. The errors resulted by VQ, viewed as non-stationarity of the data, are evaluated and an adaptive correction operation is carried out using scalar correction quatizers (SCQ). Moreover, the DC-coefficients are also vector quantized. This coding system has been simulated for natural images and an improvement of more than 3 dB is obtained in comparison to traditional DCT coding systems.

A Hybrid Coding Scheme For Still Images : Adaptive Vector Quantization With Correction Information In Dct Domain

first, a DCT (or Hadamard) trans-form is performed on these blocks. A classification algorithm ranks them into visual andtransform properties -based classes. For each class, high energy carrying coefficients are re-tained and using vector quantization, a codebook is built for the AC remaining part of thetransformed blocks. The whole of the codeworks are referenced by an index. Each block is thencoded by specifying its DC coefficient and associated index.IntroductionTV images contain many similar areas than can be coded with a unique representation. Fur-ther more, including visual perception criteria of these areas in the coder, one can selectthe appropriate minimum information to be transmitted so that the distortion between originaland reconstructed image is minimum. Vector quantization, as demonstrated recently (1), (2),(3) can remove spatial redundancies between blocks of pixels.In this paper, this method is applied in a transform domain to take advantage of some in-teresting properties. A general coding scheme is given in figure 1

J. P. .. Marescq

Papers

Image Coding By Vector Quantization In A Transformed Domain