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An Innovative Recompression Scheme for VQ Index Tables

Author

Listed:
  • Yijie Lin

    (Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan)

  • Jui-Chuan Liu

    (Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan)

  • Ching-Chun Chang

    (Information and Communication Security Research Center, Feng Chia University, Taichung 40724, Taiwan)

  • Chin-Chen Chang

    (Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan)

Abstract

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods.

Suggested Citation

  • Yijie Lin & Jui-Chuan Liu & Ching-Chun Chang & Chin-Chen Chang, 2024. "An Innovative Recompression Scheme for VQ Index Tables," Future Internet, MDPI, vol. 16(8), pages 1-19, August.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:8:p:297-:d:1459344
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