No-reference quality assessment of deblocked images

JPEG is the most commonly used image compression standard. In practice, JPEG images are easily subject to blocking artifacts at low bit rates. To reduce the blocking artifacts, many deblocking algorithms have been proposed. However, they also introduce certain degree of blur, so the deblocked images contain multiple distortions. Unfortunately, the current quality metrics are not designed for multiply distorted images, so they are limited in evaluating the quality of deblocked images. To solve the problem, this paper presents a no-reference (NR) quality metric for deblocked images. A DeBlocked Image Database (DBID) is first built with subjective Mean Opinion Score (MOS) as ground truth. Then a NR DeBlocked Image Quality (DBIQ) metric is proposed by simultaneously evaluating blocking artifacts in smooth regions and blur in textured regions. Experimental results conducted on the DBID database demonstrate that the proposed metric is effective in evaluating the quality of deblocked images, and it significantly outperforms the existing metrics. As an application, the proposed metric is further used for automatic parameter selection in image deblocking algorithms.