Locally adaptive perceptual quantization without side information for compression of visual data

This paper presents a locally adaptive perceptual quantization scheme for visual data compression. The strategy is to exploit human visual masking properties by deriving masking thresholds in a locally adaptive fashion based on a sub-band decomposition. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels to the local amount of masking present at the level of each sub-band transform coefficient. Compared to the existing non-locally-adaptive perceptual quantization methods, the new locally adaptive algorithm exhibit superior performance and does not require additional side information. This is accomplished by estimating the amount of available masking from the already quantized data and linear prediction of the coefficient under consideration. By virtue of the local adaptation, the proposed quantization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side information, it yields a low entropy representation of the image and is well suited for perceptually-lossless image compression.