1 October 1990 Perceptually tuned sub-band image coder
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In this paper we present a sub-band coder for true color images that uses an empirically derived perceptual masking model to set the allowable quantization noiselevel not only for each sub-band but also for each pixel in a given sub-band. The input image is converted into YIQ space and each channel is passed through a separable Generalized Quadrature Mirror Filterbank (GQMF). This separates the image's frequency content into into 4 equal width bands in both the horizontal and vertical dimension, resulting in a representation consisting of 16 sub-bands for each channel. Using this representation, a perceptual masking model is derived for each channel. The model incorporates spatial-frequency sensitivity, contrast sensitivity, and texture masking. Based on the image dependent information in each sub-band and the perceptual masking model, noise-level targets are computed for each point in a subband. These noise-level targets are used to set the quantization levels in a DPCM quantizer. The output from the DPCM quantizer is then encoded, using an entropybased coding scheme, in either lxi , 1x2, or 2x2 pixel parts, based on the the statistics in each 4x4 sub-block of a particular sub-band. One set of codebooks, consisting of 100,000 entries, is used for all images. A block elimination algorithm takes advantage of the peaky spatial energy distribution of sub-bands to avoid using bits for quiescent parts of a given sub-band. The resultant bitrate depends on the complexity of the input image. For the images we use, high quality output requires bitrates from 0.25 to 1 .25 bits/pixel, while nearly transparent quality requires 0.5 to 2.5 bits/pixel.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert J. Safranek, Robert J. Safranek, James D. Johnston, James D. Johnston, Ruth Ellen Rosenholtz, Ruth Ellen Rosenholtz, } "Perceptually tuned sub-band image coder", Proc. SPIE 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications, (1 October 1990); doi: 10.1117/12.19678; https://doi.org/10.1117/12.19678

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