Abstract: 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.