Spatial quantization via local texture masking

Wavelet-based transform coding is well known for its utility in perceptual image compression. Psychovisual modeling has lead to a variety of perceptual quantization schemes, for efficient at-threshold compression. Successfully extending these models to supra-threshold compression, however, is a more difficult task. This work attempts to bridge the gap between at threshold modeling and supra-threshold compression by combining a spatially-selective quantization scheme, designed for at-threshold compression with simple MSE-based rate-distortion optimization. A psychovisual experiment is performed to determine how textured image regions can be used to mask quantization induced distortions. Texture masking results from this experiment are used to derive a spatial quantization scheme, which hides distortion in high-contrast image regions. Unlike many spatial quantizers, this technique requires explicit side information to convey contrast thresholds to generate step-sizes. A simple coder is presented that is designed that applies spatially-selective quantization to meet any rate constraints near and above threshold. This coder leverages this side information to reduce the rate required to code the quantized data. Compression examples are compared with JPEG-2000 examples with visual frequency weighting. When matched for rate, the spatially quantized images are highly competitive with and in some cases superior to the JPEG-2000 results in terms of visual quality.