Forward-adaptive quantization with optimal overhead cost for image and video coding with applications to MPEG video coders

Antonio Ortega; Kannan Ramchandran

doi:10.1117/12.206352

17 April 1995 Forward-adaptive quantization with optimal overhead cost for image and video coding with applications to MPEG video coders

Antonio Ortega, Kannan Ramchandran

Author Affiliations +

Proceedings Volume 2419, Digital Video Compression: Algorithms and Technologies 1995; (1995) https://doi.org/10.1117/12.206352
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

We address the problem of optimal forward-adaptive quantization in the video and image coding framework. In this framework, as is consistent with that of most practical coders like MPEG, the encoder has the capability of changing the quantizer periodically (e.g. at a macroblock interval in MPEG). In this paper, we formulate an optimal strategy, based on dynamic programming, for updating the quantizer choice for coding an image or video signal. While in some coding environments the overhead needed to specify the quantizer used by each block is equal for every choice of quantizer, in other situations (e.g. MPEG) the overhead cost is higher if the quantizer changes from one block to the next. We concentrate on the latter case which will be more likely encountered in situations where the overhead represents a significant fraction of the overall rate, as can be the case if a low bit rate is used (e.g. error frames in a typical motion-compensated video coder). We provide empirical evidence of the performance gain that can be obtained when applying our optimal algorithm to typical motion-compensated prediction error frames in MPEG, showing how the popular Viterbi algorithm can be used to find the optimal solution.

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Antonio Ortega and Kannan Ramchandran "Forward-adaptive quantization with optimal overhead cost for image and video coding with applications to MPEG video coders", Proc. SPIE 2419, Digital Video Compression: Algorithms and Technologies 1995, (17 April 1995); https://doi.org/10.1117/12.206352

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