Device for transmitting and/or storing signals presented thereto representing the textures of different areas of a video picture. The device includes a sampling stage for deriving a reference sample of the texture signals from a given picture area, and a coding stage for coding the reference sample signal as well as area signals representative of texture over the entire area from which the sample has been taken. The coded output signals of the coding stage may be transmitted by a transmission medium such as a communication channel and/or a store. A decoding stage then decodes the coded signals, and a texture synthesis stage uses the decoded sample signals to enhance the decoded area signals, thereby effectively achieving synthesis of a reproduction of the relevant picture area having a texture closely resembling the texture of such area of the original picture.

System for transmitting and/or storing signals corresponding to textured pictures

A novel optimal adaptive scalar quantization method is proposed, and its performance is investigated in the context of quantization for image compression. The idea is to perform frequent and complete (non-incremental) block-optimal quantizer redesign, but under a codebook constraint which assures that the overhead required to specify the new quantizer is small. The intuition is that a few levels are usually sufficient to accurately describe a small image block, provided these are chosen optimally and independently for each block. In addition to limiting overhead, the codebook constraint can be exploited to derive an efficient dynamic programming algorithm for block-optimal quantizer design. Some experimental results and comparisons are also included.

Optimal adaptive scalar quantization and image compression

A sample selection procedure for a subband vector-quantization (VQ) interframe predictive coder incorporating short-kernel subband filter banks is presented. The coder is based on two-dimensional subband decomposition of the error image obtained after motion-compensated interframe prediction (MCIP). The luminance and chrominance subbands, generated after the decomposition process, form image vectors which are then quantized using conventional vector quantization. By using the sample selection procedure, selective quantization of the image vectors can be carried out with minimal distortion introduced into the reconstructed images. Any major spatial redundancies in the MCIP error image are exploited to achieve the low output bit rate desired. >

Sample selection in subband vector quantization

The standard fractal image coding algorithm consists of, given any discrete image specified by an array of pixels, determining a mapping of contractive transformations where the decoded image is visually close to the original one. In this work, the original formulation for discrete images to the space of continuous images based on B-splines is extended. By selecting an appropriate B-spline order n, visually improved images may he obtained, compared to the discrete image approach. This lies on the fact that the blockiness of the edges of the range blocks is diminished due to the entanglement of the B-spline base and the continuity of spline functions. Moreover, the PSNR value of the reconstructed image may be increased at no additional information of the fractal coding and slightly more calculation realized on the space of B-spline coefficients. The results were obtained using a real digital image.

Fractal image coding for images using B-splines

Medical images compression by neural networks.

A new VQ-based image coding method, classified vector quantization using texture analysis (CVQ-TA) is proposed. The most notable differences between CVQ-TA and CVQ are: (1) the classification in CVQ-TA operates in the transform domain by means of texture analysis, and (2) product-code vector quantization is used to reduce the complexity increasing with the subblock size. Because of these characteristics, CVQ-TA can be applied for large block size (8*8 or 16*16), which is more efficient for block coding. Simulation results show that CVQ-TA can give a good visual perceptual quality. >

http://ieeexplore.ieee.org/iel5/143/3356/00112276.pdf

L. Li

Papers

Classified vector quantization of images using texture analysis