An optimization approach for removing blocking effects in transform coding

Summary form only given. This paper proposes a new approach which can be applied to conventional transform coding without introducing additional information or significant blurring. It exploits the correlation between intensity values of boundary pixels of two neighboring blocks. Specifically, it is based on the theoretical and empirical observation that under mild assumptions, quantization of transform coefficients of two neighboring blocks increases the mean squared difference (MSD) between neighboring pixels on their boundaries by an amount dependent upon the width of quantization intervals of the transform coefficients. Therefore, among all permissible inverse quantized coefficients, the set which minimizes this MSD is most likely to reduce the blocking effect. To estimate the set of unquantized coefficients, a constrained quadratic programming problem is solved in which the quantization decision intervals provide upper and lower bound constraints on the coefficients. Computer simulations are used to evaluate the performance of the proposed technique for the 256×256 image known as Lena