17 April 1995 Block adaptive classified vector quantization
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As vector quantization (VQ) under low bit-rate constraint suffers serious reconstruction degradation, block adaptive classified VQ has been considered as an effective scheme not only to improve reconstructed image quality but also to reduce processing time. These desirable properties are achieved mainly by combining adaptive block segmentation with classified VQ, which results in a better adaptation of the coding process to the nature of images. Central to this scheme is the classification process. Its operation is based on both transform and spatial domains to achieve reasonable classification accuracy and simplicity. In making adaptive segmentation decisions, low-ordered DCT coefficients and dynamic range of pixel values are used to establish variable size blocks. For classified VQ, low-ordered DCT coefficients and contrast sensitivity are employed to characterize various edge orientations and positions within image blocks, respectively. An alternative approach for determining suitable codebook sizes to be used with classified VQ has been investigated with favorable trade-off between overall distortion and processing time. Following the block segmentation and classification, all image blocks are coded with VQ in the spatial domain to take advantage of its better rate-distortion performance. Reconstructed images with peak signal-to-noise ratios ranging from 28.2 to 35.3 dB have been obtained at coding rates between 0.27 and 0.46 bit-per-pixel.
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Huy So Peter Truong, Huy So Peter Truong, Stephen C.Y. Ho, Stephen C.Y. Ho, "Block adaptive classified vector quantization", Proc. SPIE 2419, Digital Video Compression: Algorithms and Technologies 1995, (17 April 1995); doi: 10.1117/12.206381; https://doi.org/10.1117/12.206381

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