Image Compression with Anisotropic Diffusion
Journal of Mathematical Imaging and Vision
Improving BTC image compression using a fuzzy complement edge operator
Signal Processing
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Improved edge preserving lossy image compression using wavelet transform
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Improved BTC algorithm for gray scale images using k-means quad clustering
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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In this paper, we present a static image compression algorithm for very low bit rate applications. The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information. Since the human visual system is highly sensitive to edges, an edge-based compression scheme can produce intelligible images at high compression ratios. We present good quality results for facial as well as textured, 256~x~256 color images at 0.1 to 0.3 bpp. The algorithm described in this paper was designed for high performance, keeping hardware implementation issues in mind. In the next phase of the project, which is currently underway, this algorithm will be implemented in hardware, and new edge-based color image sequence compression algorithms will be developed to achieve compression ratios of over 100, i.e., less than 0.12 bpp from 12 bpp. Potential applications include low power, portable video telephones.