The week's pick
Reseach Article
Constrain Study of Basic Vector Quantization Techniques for Image Compression
| International Conference on Advances in Communication and Computing Technologies 2012 |
| Foundation of Computer Science USA |
| ICACACT - Number 2 |
| August 2012 |
| Authors: Bhargav Makodia, Arpit Patel, Tushar Patel |
| def2855b-97eb-4979-8f68-3aaac3e73210 |
Bhargav Makodia, Arpit Patel, Tushar Patel . Constrain Study of Basic Vector Quantization Techniques for Image Compression. International Conference on Advances in Communication and Computing Technologies 2012. ICACACT, 2 (August 2012), 1-4.
Abstract
Vector Quantization (VQ) is one of the lossy image compression techniques. VQ comprises of three different entities: codebook generation, image encoding and image decoding. In this paper three different VQ techniques namely Mean Remove Vector Quantization (MRVQ), Shape Gain Vector Quantization (SGVQ) and Classified Vector Quantization (CVQ) has been discussed and their performance in terms of Signal to Noise ratio (SNR) is compared. Lloyd algorithm is used for optimal codebook generation.
References
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