Block truncation coding (BTC) technique is a simple and fast image compression algorithm since complicated transforms are not used. The principle used in BTC algorithm is to use two‐level quantiser that adapts to loc...
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Block truncation coding (BTC) technique is a simple and fast image compression algorithm since complicated transforms are not used. The principle used in BTC algorithm is to use two‐level quantiser that adapts to local properties of the image while preserving the first‐ or first‐ and second‐order statistical moments. The parameters transmitter or stored in the BTC algorithm are statistical moments and bitplane yielding good quality images at a bitrate of 2 bits per pixel (bpp). In this paper, two algorithms for modified BTC (MBTC) are proposed for reducing the bitrate below 2 bpp. The principal used in the proposed algorithms is to use the ratio of moments which is a smaller value when compared to absolute moments. The ratio values are then entropy coded. The bitplane is also coded to remove the correlation among the bits. The proposed algorithms are compared with MBTC and the algorithms obtained by combining JPEG standard with MBTC in terms of bitrate, peak signal‐to‐noise ratio (PSNR) and subjective quality. It is found that the reconstructed images obtained using the proposed algorithms yield better results.
In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images have become a real problem. Image compression is the first approach to address this proble...
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In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images have become a real problem. Image compression is the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon information on its pixels that are transmitted progressively. We consider this transmission as a dynamical process, where the sender pushes the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting of<
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