High resolution and image compression using the discrete cosine transform

Stanley A. Klein

doi:10.1117/12.19671

1 October 1990 High resolution and image compression using the discrete cosine transform

Stanley A. Klein

Proceedings Volume 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications; (1990) https://doi.org/10.1117/12.19671
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States

Abstract

The information gathering capacity of the visual system can be specified in units of bits/mm2. The fall-off in sensitivity of the human visual system at high spatial frequencies allows a reduction in the bits/mm2 needed to specify an image. A variety of compression schemes attempt to achieve a further reduction in the number of bit/mm2 while maintaining perceptual losslessness. This paper makes the point that whenever one reports the results of an image compression study, numbers should be provided. The first is the number of bits/mm2 that can be achieved using properties of the human visual system, but ignoring the redundancy of the image (entropy coding). The second number is the bits/mm2 including the effects of entropy coding. The first number depends mainly on the properties of the visual system, the second number includes, in addition, the properties of the image. The Discrete Cosine Transform (DCT) compression method is used to determine the first number. It is shown that the DCT requires between 16 and 24 bits/mm2 for perceptually lossless encoding of images, depending on the size of the blocks into which the image is subdivided. In addition, the efficiency of DCT compression is found to be limited by its susceptibility to interference from adjacent maskers. The present analysis suggests that the visual system requires many more bits/mm2 than the results of other researchers who find that .5 bits/mm2 are sufficient to represent an image without perceptible loss.

Citation Download Citation

Stanley A. Klein "High resolution and image compression using the discrete cosine transform", Proc. SPIE 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications, (1 October 1990); https://doi.org/10.1117/12.19671

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