Title :
Optimal adaptive scalar quantization and image compression
Author :
Sidiropoulos, Nicholas D.
Author_Institution :
Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA
Abstract :
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
Keywords :
adaptive signal processing; data compression; dynamic programming; image coding; quantisation (signal); block-optimal quantizer redesign; codebook constraint; dynamic programming algorithm; experimental results; image block; image compression; optimal adaptive scalar quantization; overhead; performance; Algorithm design and analysis; Availability; Cost function; Decoding; Head; Image coding; Iterative algorithms; Random variables; Training data; Vector quantization;
Conference_Titel :
Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-8186-8821-1
DOI :
10.1109/ICIP.1998.723527