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Development of Multi-Image Compression Technique Based on Common Code Vector

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Veröffentlicht in:SN Computer Science vol. 4, no. 1 (Jan 2023), p. 31
1. Verfasser: Barman, Dibyendu
Weitere Verfasser: Hasnat, Abul, Barman, Bandana
Veröffentlicht:
Springer Nature B.V.
Schlagworte:
Algorithms
Image compression
Mathematical analysis
Image quality
Data compression
Color imagery
Compression ratio
Parameters
Optimization techniques
Signal to noise ratio
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Abstract:In the field of data compression, the performance of an image compression technique based on the amount of compression ratio achieved keeps the visual quality of the decompressed image as close to the original as possible. In conventional vector quantization techniques, the size of the code vector plays an important role in measuring the amount of space required to store an image. The compression ratio of the method decreases as the size of the code vector increases. The current study proposes a new image compression technique that generates a common code vector for a number of images of the same or different sizes by adjusting some tuning parameters. This common code vector holds a unique code word for each and every image. At the same time, index matrices are updated according to the index value of the common code vector. The images are decompressed using the respective index matrix and the common code vector. So, in this work, for the same or different sizes of images, only one common code vector is generated. The size of the common code vector is much less compared to the total size of the individual code vectors. Hence, it achieves a very high compression ratio. The proposed method is applied to many standard images found in literature and images from the UCIDv.2 color image database. Experimental results are analyzed in terms of peak signal to noise ratio (PSNR), structure similarity index parameter (SSIM), and compression ratio. The experimental result shows that the proposed method achieved an average of 95.12% compression ratio, which is 3.51% higher than the conventional vector quantization algorithm and 7.42% higher than the existing modified vector quantization technique, keeping the visual quality of the decompressed image almost the same as those two image compression algorithms.
ISSN:2662-995X
2661-8907
DOI:10.1007/s42979-022-01450-0
Quelle:Advanced Technologies & Aerospace Database