Block Truncation Coding

Abstract: The present work investigates image compression using block truncation coding. Two algorithms were selected namely, the original block truncation coding (BTC) and Absolute Moment block truncation coding (AMBTC) and a comparative study was performed. Both of two techniques rely on applying divided image into non overlapping blocks. They differ in the way of selecting the quantization level in order to remove redundancy. Objectives measures were used to evaluate the image quality such as: Peak Signal to Noise Ratio (PSNR), Weighted Peak Signal to Noise Ratio (WPSNR), Bit Rate (BR) and Structural Similarity Index (SSIM).The results have shown that the ATBTC algorithm outperforms the BTC. It has been show that the image compression using AMBTC provides better image quality than image compression using BTC at the same bit rate. Moreover, the AMBTC is quite faster compared to BTC Index Terms—BTC, AMBTC, WPSNR, SSIM.

Abstract: An image compression system and method for compressing image data for transmission Each block and corresponding sub-blocks of pixel data is subjected to a discrete cosine transform (DCT) operation Varying levels of sub-blocks of resulting corresponding transform coefficients are selected for construction into a composite transform coefficient block corresponding to each input block of pixel data The selection of transform coefficient block size for the composite block is determined by a comparison process between transform block and sub-block coding efficiency The composite block is variable length coded to further reduce bit count in the compressed data

Abstract: A new quantization method that uses the criterion of preserving sample absolute moments is presented. This is based on the same basic idea for block truncation coding of Delp and Mitchell but it is simpler in any practical implementation. Moreover, output equations are those for a two-level nonparametric minimum mean square error quantizer when the threshold is fixed to the sample mean. The application of this method to single frame color images is developed. A color image coding system that uses absolute moment block truncation coding of luminance and chroma information is presented. Resulting color images show reasonable performance with bit rates as low as 2.13 bits/pixel.

Abstract: Block transform coded images usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. In this paper, we propose a new method to reduce compression artifacts by the overlapped-block transform coefficient estimation from non-local blocks. In the proposed method, the discrete cosine transform coefficients of each block are estimated by adaptively fusing two prediction values based on their reliabilities. One prediction is the quantized values of coefficients decoded from the compressed bitstream, whose reliability is determined by quantization steps. The other prediction is the weighted average of the coefficients in nonlocal blocks, whose reliability depends on the variance of the coefficients in these blocks. The weights are used to distinguish the effectiveness of the coefficients in nonlocal blocks to predict original coefficients and are determined by block similarity in transform domain. To solve the optimization problem, the overlapped blocks are divided into several subsets. Each subset contains nonoverlapped blocks covering the whole image and is optimized independently. Therefore, the overall optimization is reduced to a set of sub-optimization problems, which can be easily solved. Finally, we provide a strategy for parameter selection based on the compression levels. Experimental results show that the proposed method can remarkably reduce compression artifacts and significantly improve both the subjective and objective qualities of block transform coded images.