While product code VQ is an effective paradigm for reducing the encoding search and memory requirements of vector quantization, a significant limitation of this approach is the heuristic nature of bit allocation among the product code features. We propose an optimal bit allocation strategy for PCVQ through the explicit incorporation of an entropy constraint within the product code framework. Unrestricted entropy-constrained PCVQs require joint entropy codes over all features and concomitant encoding and memory storage complexity. To retain manageable complexity, we propose "product-based" entropy code structures, including independent and conditional feature entropy codes. We also propose an iterative, locally optimal encoding strategy to improve performance over greedy encoding at a small cost in complexity. This approach is applicable to a large class of product code schemes, allowing joint entropy coding of feature indices without exhaustive encoding. Simulations demonstrate performance gains for image coding based on the mean-gain-shape product code structure.<>
Entropy-constrained product code vector quantization with application to image coding
Proceedings of 1st International Conference on Image Processing ; 1 ; 623-627 vol.1
01.01.1994
576188 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Entropy-Constrained Product Code Vector Quantization with Application to Image Coding
British Library Conference Proceedings | 1994
|Adaptive Vector Quantization for Image Coding in an Entropy-Constrained Framework
British Library Conference Proceedings | 1994
|British Library Online Contents | 2000
|Conditional Entropy-Constrained Vector Quantization of Displaced Frame Difference Subband Signals
British Library Conference Proceedings | 1994
|