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Hybrid Image Compression using Modified Singular Value Decomposition and Adaptive Set Partitioning in Hierarchical Tree
Objectives: Image communication in web applications becomes handy because of highly developed compression tools. Human eye fixate on an image’s preview, carefully adjusting the quality and optimization settings until we’ve found that sweet spot, where the file size and quality are both the best they can possibly be. Method: This paper presents a new algorithm, which uses modified singular value decomposition (SVD) and adaptive Set Partition Hierarchical Tree (ASPIHT) for grayscale image compression. This hybrid method uses modified rank one updated SVD as a pre-processing step for ASPIHT to increase the quality of the reconstructed image. Findings: The high energy compaction in SVD process offers high image quality with less compression and requires a number of bits for reconstruction. On the other hand, ASPIHT compression also offers high image quality by coding more significant coefficients adaptively with high compression at specified bitrates. The proposed method is a combination of both SVD and ASPIHT for image compression and is tested with several test images and results are compared with those of SPIHT, ASPIHT without arithmetic coding and JPEG2000. Novelty: This method improves the quality of reconstruction without altering the compression rates of SPIHT algorithm. The tabulated results show significant improvement in PSNR at higher compression ratios as compared to other methods.
Adaptive Set Partitioned Hierarchical Tree, Image Compression, Rank One Updated Singular Value Decomposition
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