New adaptive technique for color image compression

Hesham Farouk; Mohsen Rashwan; Samia A. Mashali; Amin Nassar

doi:10.1117/12.258281

14 November 1996 New adaptive technique for color image compression

Hesham Farouk, Mohsen Rashwan, Samia A. Mashali, Amin Nassar

Proceedings Volume 2847, Applications of Digital Image Processing XIX; (1996) https://doi.org/10.1117/12.258281
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

We proposed a composite adaptive technique for color image compression. We apply a mapping transform from (R, G, B) space to (Y_d, C_r, C_b) to represent the image as luminance image. The proposed compression technique is based on discrete cosine transform (DCT) with block size of 8 by 8 pixels. The proposed adaptive DCT technique depends on the maximum luminance value and the difference between this maximum and the minimum one in each block. This will help to select one quantization matrix out of 16 different matrices. The quantization matrices represent the whole range of types of the blocks. As an error correction step we create a 3D matrix that represents the whole image. The X axis is the number of blocks per row, the Y axis is the number of blocks per column and the Z axis is the 256 colors of the image. This will replace the coding of C_r, C_b components and it will solve the problem is case where the luminance value represents more than one color. The technique achieves a high compression ratio in the range of 0.65 bpp for Y_d component of the smooth image to 1 bpp for detailed image in addition to overheads in the range from 0.15 to 0.25 bpp for the error correction matrix. The technique gives a higher compression ratio compared ratio compared to the well known technique for standard images, such as Lenna, while maintaining the same quality.

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Hesham Farouk, Mohsen Rashwan, Samia A. Mashali, and Amin Nassar "New adaptive technique for color image compression", Proc. SPIE 2847, Applications of Digital Image Processing XIX, (14 November 1996); https://doi.org/10.1117/12.258281

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KEYWORDS

Image compression

Quantization

3D image processing

Image quality

Matrices

Signal to noise ratio

Chromium

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