TY - JOUR
T1 - Entropy-constrained product code vector quantization with application to image coding
AU - Lightstone, M.
AU - Miller, D.
AU - Mitra, S. K.
N1 - Funding Information:
This work was supported in part by a National Science Foundation Graduate Research Fellowship and in part by a University of California MICRO grant with matching support from Digital Instruments, Rockwell International and Tektronix.
Publisher Copyright:
© 1994 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 1994
Y1 - 1994
N2 - 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.
AB - 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.
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U2 - 10.1109/ICIP.1994.413389
DO - 10.1109/ICIP.1994.413389
M3 - Conference article
AN - SCOPUS:84997503373
VL - 1
SP - 623
EP - 627
JO - Proceedings - International Conference on Image Processing, ICIP
JF - Proceedings - International Conference on Image Processing, ICIP
SN - 1522-4880
M1 - 413389
T2 - Proceedings of the 1994 1st IEEE International Conference on Image Processing. Part 3 (of 3)
Y2 - 13 November 1994 through 16 November 1994
ER -