The notion of perceptual coding, which is based on the concept of distortion masking by the signal being compressed, is developed. Progress in this field as a result of advances in classical coding theory, modeling of human perception, and digital signal processing, is described. It is proposed that fundamental limits in the science can be expressed by the semiquantitative concepts of perceptual entropy and the perceptual distortion-rate function, and current compression technology is examined in that framework. Problems and future research directions are summarized. >

Signal compression based on models of human perception

To represent an image of high perceptual quality with the lowest possible bit rate, an effective image compression algorithm should not only remove the redundancy due to statistical correlation but also the perceptually insignificant components from image signals. In this paper, a perceptually tuned subband image coding scheme is presented, where a just-noticeable distortion (JND) or minimally noticeable distortion (MND) profile is employed to quantify the perceptual redundancy. The JND profile provides each signal being coded with a visibility threshold of distortion, below which reconstruction errors are rendered imperceptible. Based on a perceptual model that incorporates the threshold sensitivities due to background luminance and texture masking effect, the JND profile is estimated from analyzing local properties of image signals. According to the sensitivity of human visual perception to spatial frequencies, the full-band JND/MND profile is decomposed into component JND/MND profiles of different frequency subbands. With these component profiles, perceptually insignificant signals in each subband can be screened out, and significant signals can be properly encoded to meet the visibility threshold. A new quantitative fidelity measure, termed as peak signal-to-perceptible-noise ratio (PSPNR), is proposed to assess the quality of the compressed image by taking the perceptible part of the distortion into account. Simulation results show that near-transparent image coding can be achieved at less than 0.4 b/pixel. As compared to the ISO-JPEG standard, the proposed algorithm can remove more perceptual redundancy from the original image, and the visual quality of the reconstructed image is much more acceptable at low bit rates.

A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile

The applications of digital data compression and the major components of compression systems are described. Data modeling is discussed, and the role of entropy and data statistics is examined. Gray-scale image modeling is used to illustrate some of these mechanisms. The coding mechanisms are examined, and prefix codes are explained. Arithmetic coding is considered. >

https://ieeexplore.ieee.org/iel1/45/115/00001889.pdf

Data compression

To maintain high image quality with low bit-rates, an effective coding algorithm should not only remove statistical correlation but also perceptual redundancy from image signals. A subband coding scheme based on estimation and exploitation of just-noticeable-distortion (JND) profile is presented. >

An image compression system and method for compressing image data for transmission Each block and corresponding sub-blocks of pixel data is subjected to a discrete cosine transform (DCT) operation Varying levels of sub-blocks of resulting corresponding transform coefficients are selected for construction into a composite transform coefficient block corresponding to each input block of pixel data The selection of transform coefficient block size for the composite block is determined by a comparison process between transform block and sub-block coding efficiency The composite block is variable length coded to further reduce bit count in the compressed data

Adaptive block size image compression method and system

An efficient single-pass adaptive bandwidth compression technique using the discrete cosine transform is described. The coding process involves a simple thresholding and normalization operation on the transform coefficients. Adaptivity is achieved by using a rate buffer for channel rate equalization. The buffer status and input rate are monitored to generate a feedback normalization factor. Excellent results are demonstrated for coding of color images at 0.4 bits/pixel corresponding to real-time color television transmission over a 1.5 Mbit/s channel.

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