Dynamic contrast-based quantization for lossy wavelet image compression

• Published in: IEEE transactions on image processing 2005-04, Vol.14 (4), p.397-410
• Main Authors: Chandler, D.M., Hemami, S.S.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Bit rate; Computer Graphics; contrast; Data Compression - methods; Detection, estimation, filtering, equalization, prediction; Discrete wavelet transforms; Displays; Distortion measurement; Exact sciences and technology; Extraterrestrial measurements; Human visual system (HVS); Image coding; image compression; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image reconstruction; Information, signal and communications theory; JPEG-2000; Multimedia; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated - methods; Psychology; Quantization; Reproducibility of Results; Sampling, quantization; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Telecommunications and information theory; Visual system; wavelet
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2004.841196

This paper presents a contrast-based quantization strategy for use in lossy wavelet image compression that attempts to preserve visual quality at any bit rate. Based on the results of recent psychophysical experiments using near-threshold and suprathreshold wavelet subband quantization distortions presented against natural-image backgrounds, subbands are quantized such that the distortions in the reconstructed image exhibit root-mean-squared contrasts selected based on image, subband, and display characteristics and on a measure of total visual distortion so as to preserve the visual system's ability to integrate edge structure across scale space. Within a single, unified framework, the proposed contrast-based strategy yields images which are competitive in visual quality with results from current visually lossless approaches at high bit rates and which demonstrate improved visual quality over current visually lossy approaches at low bit rates. This strategy operates in the context of both nonembedded and embedded quantization, the latter of which yields a highly scalable codestream which attempts to maintain visual quality at all bit rates; a specific application of the proposed algorithm to JPEG-2000 is presented.