A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains. >

Image coding using vector quantization: a review

In an encoder responsive to a sequence of coefficient blocks each of which is composed of a plurality of orthogonal transform coefficients subjected to predictive encoding and orthogonal transform, the coefficient blocks are classified by a classification circuit (55) into a plurality of classes to produce a control signal (CONT) representative of the classes. A quantization unit (60), a code conversion unit (61), and a coefficient selection unit (62, 64) are controlled by the control signal to select quantization characteristics, code conversion characteristics, and coefficient selection characteristics. The quantization unit comprises a plurality of quantizers corresponding to the quantization characteristics while the code conversion unit and the coefficient selection unit comprise a plurality of code converters and a plurality of selection tables corresponding to the code conversion characteristics and the coefficient selection characteristics, respectively. Consequently, the coefficients are adaptively encoded into a reduced amount of encoded codes on encoding a moving image.

Encoder capable of faithfully and adaptively encoding a moving image

Performance analysis from rate distortion theory of wavelet domain vector quantization encoding

This paper discusses the design of a matrix quantizer incorporating the human visual model for image encoding. It is well known that in image processing square error distortion measure is not the most suitable yardstick for the evaluation of subjective quality of reconstructed images. Since in almost all image data compression systems the human observer is the ultimate destination, one may take into account some parameters of the human visual system in computing the distance measure used in the design of an encoding algorithm. The matrix quantizer design and encoding algorithms have been modified to include a reasonably accurate model to reflect the human visual perception process. It is found that the reconstructed images in this case are sharper and exhibit much less staircase effect usually found in matrix quantizers that do not include such a model. The results were compared for rate one and 0.56 bits per pixel (bpp) with those of straight matrix quantizers of similar rate and size.

A matrix quantizer incorporating the human visual model

This paper discusses a multi-stage matrix quantizer with a tree structure for image encoding wherein the number of searches required is a linear function of the product of the matrix size and the bit rate rather than an exponential one as in the case of a full search encoder. A two-step nine-way classification procedure is employed in order to preserve the edge contents of images. Further data rate reduction is achieved by preprocessing the images with a simple linear Hadamard transformer. A kl subblock of the input image is classified nine ways to detect the edge orientation. If it is not an edge a shade codebook is designed. Eight different codebooks are generated to accommodate each of the edge orientations under consideration. It is observed that the staircase degradation due to quantization is greatly reduced when edge detection is used. It is also found that the degradation due to tree encoding is only about 0.5 dB as compared to full search encoding.

A fast matrix quantizer for image encoding

This paper discusses a matrix quantizer design algorithm for image encoding problems. The design algorithm is aimed at producing a codebook of matrices which are, at least, locally optimum with respect to a distortion measure. We have considered the squared error distortion measure in this work and generated codebooks based on a training sequence consisting of a number of pictures of different bit rates. The preliminary results show promise for further work in this direction.

H. Bheda

Papers

A matrix quantizer for image processing

A fast matrix quantizer for image encoding