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27 February 1996 Adaptive cosine transform image coding with variable block size and constant block distortion
David G. Daut, Jia-Chyi Wu
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Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233183
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
Abstract
An adaptive discrete cosine transform (DCT) image coding system is implemented with the same average distortion designated for each variable size image block. The variable block size segmentation is performed using a quadtree data structure by dividing the perceptually more important regions of an image into smaller size blocks compared to the size of blocks containing lesser amounts of spatial activity. Due to the nonstationarity of real-world images, each image block is described by a space-varying nonstationary Gauss-Markov random field. The space-varying autoregressive parameters are estimated using an on-line modified least- squares estimator. For each assumed space-varying nonstationary image block, a constant average distortion is assigned and the code rate for each image block is allowed to vary in order to meet the fixed distortion criterion. Simulation results show that reconstructed images coded at low average distortion, based on an assumed space-varying nonstationary image model, using variable size blocks and with variable bit rate per block possess high-quality subjective (visual) and objective (measured) quality at low average bit rates. Performance gains are achieved due to the distortion being distributed more uniformly among the blocks as compared with fixed-rate, stationary image transform coding schemes.
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David G. Daut and Jia-Chyi Wu "Adaptive cosine transform image coding with variable block size and constant block distortion", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); https://doi.org/10.1117/12.233183
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KEYWORDS
Image segmentation
Distortion
Image compression
Computer programming
Image quality
Autoregressive models
Image processing
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