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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Korycki, R.: Authenticity examination of compressed audio recordings using detection of multiple compression and encodersâ identification. Forensic Sci. Int. 238, 33â46 (2014)
Chang, C.C., Chen, G.M., Lin, M.H.: Information hiding based on search-order coding for VQ indices. Pattern Recogn. Lett. 25, 1253â1261 (2004)
Lin, C.C., Liu, X.L., Yuan, S.M.: Reversible data hiding for VQ-compressed images based on search-order coding and state-codebook mapping. Inf. Sci. 293, 314â326 (2015)
Yang, C.H., Lin, Y.C.: Fractal curves to improve the reversible data embedding for VQ-indexes based on locally adaptive coding. J. Vis. Commun. Image Represent. 21, 334â342 (2010)
Chang, C.C., Nguyen, T.S., Lin, C.C.: A reversible data hiding scheme for VQ indices using locally adaptive coding. J. Vis. Commun. Image Represent. 22, 664â672 (2011)
Chang, C.C., Kieu, T.D., Wu, W.C.: A lossless data embedding technique by joint neighboring coding. Pattern Recogn. 42, 1597â1603 (2009)
Wang, J.X., Lu, Z.M.: A path optional lossless data hiding scheme based on VQ joint neighboring coding. Inf. Sci. 179, 3332â3348 (2009)
Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. 702â710 (1980)
Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. Inst. Radio Eng. 40(9), 1098â1101 (1952)
Telagarapu, P., Naveen, V.J., Prasanthi, A.L., Santhi, G.V.: Image compression using DCT and wavelet transformations. Int. J. Signal Process. Image Process. Pattern Recogn. 4, 61â70 (2011)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Âİ 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-10-6487-6_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6486-9
Online ISBN: 978-981-10-6487-6
eBook Packages: EngineeringEngineering (R0)