Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 413389 |
| Pages (from-to) | 623-627 |
| Number of pages | 5 |
| Journal | Proceedings - International Conference on Image Processing, ICIP |
| Volume | 1 |
| DOIs | |
| State | Published - Jan 1 1994 |
| Event | Proceedings of the 1994 1st IEEE International Conference on Image Processing. Part 3 (of 3) - Austin, TX, USA Duration: Nov 13 1994 → Nov 16 1994 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
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Entropy-constrained product code vector quantization with application to image coding. / Lightstone, M.; Miller, D.; Mitra, S. K.
In: Proceedings - International Conference on Image Processing, ICIP, Vol. 1, 413389, 01.01.1994, p. 623-627.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Entropy-constrained product code vector quantization with application to image coding
AU - Lightstone, M.
AU - Miller, D.
AU - Mitra, S. K.
PY - 1994/1/1
Y1 - 1994/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=84997503373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84997503373&partnerID=8YFLogxK
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
ER -