Regularized Reconstruction to Reduce Blocking Artifacts of Block Discrete Cosine Transform Compressed Images

The block discrete cosine transform (BDCT) is by far the most widely used transform for the compression of both still and sequences of images. High compression ratios are usually achieved by discarding information about the BDCT coefficients that is considered unimportant and yield images that exhibit the visually annoying blocking artifact. In this paper reconstruction of images from incomplete BDCT data is examined. The problem is formulated as one of regularized image recovery. According to this formulation, the image in the decoder is reconstructed by using not only the transmitted data but also prior knowledge about the smoothness of the original image, which complements the transmitted data. Two methods are proposed for solving this regularized recovery problem. The first is based on the theory of projections onto convex sets (POCS) while the second is based on the constrained least squares (CLS) approach. For the POCS-based method, a new constraint set is defined that conveys smoothness information not captured by the transmitted BDCT coefficients, and the projection onto it is computed. For the CLS method an objective function is proposed that captures the smoothness properties of the original image. Iterative algorithms are introduced for its minimization. Experimental results are presented that demonstrate that with the regularized reconstruction it is possible to drastically reduce the blocking artifact and improve the performance using both subjective and objective metrics of traditional decoders, which use the transmitted BDCT coefficients only.