Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Neural Networks
Low-Complexity Lossless and Fine-Granularity Scalable Near-Lossless Compression of Color Images
DCC '02 Proceedings of the Data Compression Conference
A New Lossless Compression Scheme for Medical Images by Hierarchical Segmentation
DCC '01 Proceedings of the Data Compression Conference
Vector quantization of image subbands: a survey
IEEE Transactions on Image Processing
L∞ constrained high-fidelity image compression via adaptive context modeling
IEEE Transactions on Image Processing
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Lossy image compression methods are based on error measuring at entire image level only. In some areas there is an obvious need for getting an upper bound for the error at pixel level. In the paper we propose a near-lossless compression algorithm based on DPCM simple scheme, followed by a vector quantization. The exit will have a non uniform distribution, so it will be further compressed using an entropy based method. Experimental results obtained and presented in the paper prove that the vector quantization method gives us better results than the scalar quantization and the classic LBG algorithm, in the near-lossless context.