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An efficient and novel data clustering and run length encoding approach to image compression
Author(s) -
Oswald C.,
Haritha E.,
Akash Raja A.,
Sivaselvan B.
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6185
Subject(s) - lossy compression , jpeg , cluster analysis , image compression , computer science , artificial intelligence , data compression , discrete cosine transform , encoding (memory) , lossless compression , color cell compression , pattern recognition (psychology) , compression ratio , image quality , data compression ratio , transform coding , jpeg 2000 , image (mathematics) , computer vision , data mining , image processing , internal combustion engine , automotive engineering , engineering
The paper explores the domain of lossy compression, specifically incorporating data mining techniques in the process of image encoding. Clustering is employed to group similar pixels in the image and henceforth use cluster labels in compressing the image. The proposed approach replaces the Discrete Cosine Transform phase of Conventional JPEG with a combination of clustering and Run Length Encoding so as to handle redundant data in the image effectively. Simulation with respect to benchmark data indicates improved compression (42.5%) in relation to existing solutions. Image quality metrices such as PSNR, structural similarity have also been tested and it is observed that the proposed approach achieves significant compression ratio with negligible loss in visual quality.