Abstract
Vector quantization (VQ) is an effective and important compression technique with high compression efficiency and widely used in many multimedia applications. VQ compression is a fixed-length algorithm for image block coding. In this paper, we employ the Huffman Coding technology to enhance VQ compression rate and get a better compression performance due to the reversibility of the Huffman Coding. The proposed method exploits the correlation between neighboring VQ indices with similarity. The similarity draws a large number of small differences from the current index with that of its adjacent neighbors; thereby, increasing the compression ratio due to the great quantity of small differences. The experimental results reveal that the proposed combination technique adaptively provides better compression ratios at high compression gains than that of VQ compression. The proposed method is superior in smoother pictures with the compression gains greater than 100%; even for the complex images the compression gain can be increased more than 25%. Therefore, the VQ-Huffman method can really enhance the efficiency of VQ compression.
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Acknowledgments
This research was partially supported by the Ministry of Science and Technology of the Republic of China under the Grants MOST 105-2221-E-324-014 and MOST 105-2221-E-035-051.
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Lee, CF., Chang, CC., Zeng, QF. (2018). VQ Compression Enhancer with Huffman Coding. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_13
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DOI: https://doi.org/10.1007/978-981-10-6487-6_13
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