Prioritized DCT (discrete cosine transform) image coding

Yunming George Huang; Howard M. Dreizen

doi:10.1117/12.25864

1 March 1991 Prioritized DCT (discrete cosine transform) image coding

Yunming George Huang, Howard M. Dreizen

Proceedings Volume 1396, Applications of Optical Engineering: Proceedings of OE/Midwest '90; (1991) https://doi.org/10.1117/12.25864
Event: Applications of Optical Engineering: Proceedings of OE/Midwest '90, 1990, Rosemont, IL, United States

Abstract

This paper presents a new DOT coding scheme prioritized DOT (PDCT). This scheme uses partition priority coding 8 to perform PDCT. PPC encodes DOT coefficients in the order of their (absolute) magnitude in favor of the larger coefficients without coding overhead for position information. This results in an adaptive and progressive image coding. Different from classical DOT image coding this scheme does not use any bit allocations and quantization except integer rounding. Instead this scheme utilizes multiple distribution entropy coding (MDEO). This leads to an efficient coding for progressive image transmission or compression. For PDOT an image undergoes a 8x8 or 16x16 block DOT transform and the DOT coefficients are rounded to c-bit (typically 8 bit) precision. Using PPO the coefficients on the entire image are adaptively coded. That is the largest (i. e. the most significant) coefficients are coded first followed by the smaller ones until a given bit rate is reached. Using MDEO the values and position information (required by PPO) of the ordered coefficients are packed efficiently. The PDCT coding method is shown significantly better than current lossy compression methods in (1) SNR for a given bit rate (2) Adaptivity for images other than the " class" for which the method was originally tuned and (3) Early recognition of images when used in progressive image transmission. Typical SNR results are 30. 9 dB 34. 0 dB and 36.

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Yunming George Huang and Howard M. Dreizen "Prioritized DCT (discrete cosine transform) image coding", Proc. SPIE 1396, Applications of Optical Engineering: Proceedings of OE/Midwest '90, (1 March 1991); https://doi.org/10.1117/12.25864

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KEYWORDS

Image compression

Image transmission

Signal to noise ratio

Optical engineering

Performance modeling

Image classification

Receivers

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