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Event Image Archive using Codebook Generation
Objective: To develop a system that aims at optimal storage for voluminous image data sets that are acquired during various events, by generating codebook for image clusters by exploiting the fact that these images are generally multiple shots of similar scenes at frequent intervals. Methods: Images taken during the events on various occasions in organizations are increasing exponentially and pose tremendous challenge in terms of storage and retrieval. The volume, veracity and variety of features present in image data induce complexity in computation. Optimal storage and efficient tagging will ease the retrieval process of these image data. Among various lossy image compression techniques, vector quantization yields desirable compression ratio in many applications and is one of the efficient approaches for image compression. In this research work, vector quantization technique is explored to optimize the storage space required to maintain an archive of event image data set, by generating code book for a cluster of images. Findings: The similarity in the images that are acquired during a short span of time induces redundancy. This fact has been exploited by organizing the image data set into clusters that are similar. For each cluster, vector quantization technique is used to generate code book. The codebook generated has been used to encode the image by creating an index table for each image. The codebook of the cluster and the index table of each image is further used for decoding. The compression ratio and the peak signal to noise ratio of this method are above eighty percent and thirty DB respectively. Applications: The codebook generation technique described in this work could be applied for creating image archives. Archival images are generally voluminous and consume huge storage space. The code book generated for image clusters would reduce the storage requirement. This work also finds its application in transmitting video and image clusters in a network.
Codebook, Compression, Vector Quantization, GLA, LBG
- Chen T, Chuang K. A Pseudo lossless Image Compression Method. IEEE Congress on Image and Signal Processing.2010; 2: 610–5.
- Mohsin S, Sajjad S. Codebook generation for Image Compression with Simple and Ordain GA. International Journal Of Computers And Communications. 2007; 1(2): 1–6.
- Nobuhara H, Pedrycz W, Hirota K. Fast solving method of Fuzzy relational equation and its application to lossy image compression/reconstruction. IEEE, Transactions on Fuzzy Systems. 2000 June; 8(3):325–34.
- Gupta K, Sharma M, Baweja N. Three different KG version for Image Compression. International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) IEEE, Gazihabad, India. 2014.
- Yue H, Sun X, Yang J, SIFT-Based Image Compression.IEEE, International Conference on Multimedia and Expo, Melbourne, Australia. 2012.
- Shen Y, Gao X, Liu L, Cao Q. Integer to Integer Multiwavelets for Lossless Image Compression. Proceedings of IEEE IC-BNMT, Shenzhen, China. 2011. p.217–21.
- Asmita AB, Tijare PA. A Review on LBG Algorithm for Image Compression. International Journal of Computer Science and Information Technologies. 2011; 2(6): 2584–89.
- Lu TC, Chang CY. A Survey of VQ Codebook Generatio.Journal of Information Hiding and Multimedia Signal Processing.2010 July; 1(3): 190–203.
- Tsai CW, Lee CY, Chiang MC, Yang CS. A Fast VQ Codebook Generation Algorithm via Pattern Reduction. Pattern Recognition Letters. 2009. 30: 653–60.
- Mittal M, Lamba R. Image Compression Using Vector Quantization Algorithms: A Review. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 June; 3(6): 354–8.
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