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Articles by K. Sivakami Sundari
Total Records ( 2 ) for K. Sivakami Sundari
  K. Sivakami Sundari and V. Sadasivam
  Many multimedia applications require image compression with high compression ratio to overcome the difficulties in dealing huge volume of image data. At high compression ratios, the error introduced by quantization of the transform coefficients produces visually undesirable patterns known as compression artifacts that dramatically lower the perceived quality of a particular image. Blocking artifacts of JPEG images and ringing artifacts of JPEG 2000 images plays crucial role in many applications. A great deal of effort has been invested in attempts to solve this problem while preserving the information content of the image. Proposed research primarily concentrates on the blocking artifacts of JPEG images and to a degree over the ringing artifacts of JPEG 2000 images. There exist three different approaches to reduce the artifacts as Preprocessing, Post processing and Transform domain techniques. Recently, attention is diverted to optimize the solution. To enhance the performance of the algorithm principally, the artifacts are to be detected. This in turn needs some metrics to measure these distortions. The metrics used commonly for measuring these distortions are Mean Square Error (MSE) and SNR (Signal to Noise Ratio). Current research computes the measure of blocking artifacts with the new parameter named as Total Blocking Error (TBE). Minimization of TBE is an indication about the elimination of the artifacts. This can be implemented in Transform domain with a modified quantisation table and filter. Efficient suppression of artifacts can be controlled by the scaling parameter in the quantisation process and by the kernel in the filtering process. Hence the problem can be stated as finding an optimal solution for the suppression of Artifacts with these two processes. Genetic Algorithm (GA) is one of the emerging optimization techniques. So far GA has not been used for the optimization of the reduction of artifacts. Hence an attempt is made to optimize the kernel of the filter and the scaling parameter of the quantizationwith GA. A spatial domain algorithm can enhance further the quality of the image by preserving fine details. A spatial domain algorithm can enhance further the quality of the image by preserving fine details. Dynamic range processing divide the image into luminance and chrominance component and converted to a reduced range with logarithmic mapping. Attenuating the magnitudes of large gradients processes gradient field of the luminance image. Solving a poisson equation on the modified gradient field preserves fine details. Finally the integrated in formations are remapped to the original dynamic range with inverse logarithm.
  K. Sivakami Sundari and V. Sadasivam
  Research in progress focuses the use of Genetic Algorithm (GA) and High dynamic Range image Processing (HDR) for the effective retrieval of the compressed images. The key issue for many emerging applications in the field of visual communications is the efficient compression of image data before transmission. There exists a tradeoff between compression ratio and image quality. High quality images at low bit rates can be reconstructed only by eliminating the compression artifacts. Blocking artifacts exploits the correlation between the intensity values of boundary pixels of two neighboring blocks. Specifically, it is based on the theoretical and empirical observations. Under mild assumptions, quantization of the coefficients of two neighboring blocks increases the expected value of the Mean Squared Difference of Slope (MSDS) or Mean Squared Error (MSE). Based on this, the new parameter TBE (Total Blocking effect) is computed from the compressed image using the edge differences. Minimization of TBE can be implemented in transform domain with a modified quantisation table and filter. Efficient suppression of artifacts is controlled by the scaling parameter in the quantisation process and by the kernel in the filtering process. Hence the problem can be stated as finding an optimal solution for the suppression of artifacts. Genetic algorithm is one of the emerging optimization techniques which in turn find its applications image enhancement, segmentation, fractal compression and so on. So far GA has not been used for the optimization of the artifacts at the receiver. Hence an attempt is made to optimize the kernel of the filter and the scaling parameter of the quantization with GA. A spatial domain algorithm can enhance further the quality of the image by preserving fine details. HDR processing used for this purpose divide the image into luminance and chrominance component. Process is carried out in the logarithmic domain. Proposed technique manipulates the gradient field of the luminance image by attenuating the magniudes of large gradients. Fine details are preserved by solving a Poisson equation on the modified gradient field. This novel algorithm does not affect the compressibility of the original image and is characterized by low computational complexity.
 
 
 
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