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Image coding using entropy-constrained residual vector quantization

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3 Author(s)
Kossentini, F. ; Digital Signal Process. Lab., Georgia Inst. of Technol., Atlanta, GA, USA ; Smith, M.J.T. ; Barnes, C.F.

An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relatively large vector sizes and high output rates. Experimental results indicate that good image reproduction quality can be achieved at relatively low bit rates. For example, a peak signal-to-noise ratio of 30.09 dB is obtained for the 512×512 LENA image at a bit rate of 0.145 b/p

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Image Processing, IEEE Transactions on  (Volume:4 ,  Issue: 10 )