1998Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Image compression by discrete cosine transformation

N.-E. Belkhamza, Ali Chekima, Nadjia Benblidia

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Abstract

The discrete cosine transform (DCT) is now well recognized as one of the most important technique in image data compression. Among the class of transforms possessing fast computational algorithms, the cosine transform has a superior energy compaction property. Due to its simple implementation scheme, the DCT is widely used as a substitute to the optimal Karhunen Loeve transform. In this paper, the discrete cosine transform is presented and an algorithm for its implementation is developed. The picture is firstly transform coded using 8 X 8 sub-blocks then a quantization and an entropy coding are used.

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What this paper is about

The discrete cosine transform (DCT) is now well recognized as one of the most important technique in image data compression. Among the class of transforms possessing fast computational algorithms, the cosine transform has a superior energy compaction property. Due to its simple implementation scheme, the DCT is widely used as a substitute to the optimal Karhunen Loeve transform. In this paper, the discrete cosine transform is presented and an algorithm for its implementation is developed. The picture is firstly transform coded using 8 X 8 sub-blocks then a quantization and an entropy coding are used.

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Available abstract

The discrete cosine transform (DCT) is now well recognized as one of the most important technique in image data compression. Among the class of transforms possessing fast computational algorithms, the cosine transform has a superior energy compaction property. Due to its simple implementation scheme, the DCT is widely used as a substitute to the optimal Karhunen Loeve transform. In this paper, the discrete cosine transform is presented and an algorithm for its implementation is developed. The picture is firstly transform coded using 8 X 8 sub-blocks then a quantization and an entropy coding are used.

Key concepts: Discrete cosine transform, Transform coding, Lapped transform, Modified discrete cosine transform, Discrete sine transform, Image compression, Algorithm, Data compression

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