Perceptually tuned sub-band image coder
Robert J. Safranek, James David Johnston, Ruth E. Rosenholtz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
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.