Measuring defects in images restored using DCT prediction approaches

The problem of blocking distortion reduction for images compressed using the JPEG baseline algorithms is well-known. It is because of this that the JPEG algorithm is not very effective for low-bit rate image coding applications. Over the last decade, researchers have proposed several approaches to reduce the effects of blocking artifacts, but there has been no study on the performance of the different approaches. The main problem is that no suitable metric has been established to measure the visibility of image distortions such as blockiness of block boundaries, blurriness in originally sharp edges and shifts in textured regions. Note that the mean-square-error (MSE) metric is not suitable for this purpose. In this article, first we outline the discrete cosine transform (DCT) prediction approach for the blocking artifacts reduction problem, and we summarize four different implementations of this approach. Next, we define a number of new metrics by considering various types of distortions resulting from the use of a block-based coding technique, and finally we present results of experiments that we have conducted to compare the performance of the four implementations. The reasons for choosing the prediction approach are that it is used by the JPEG´s block smoothing algorithm, that it can be implemented easily in hardware, and that it can be used for real-time applications such as the browsing of images over a network