ABSTRACT
We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index, named PSNR-B, that produces objective judgments that accord with observations. The PSNR-B modifies PSNR by including a blocking effect factor. We also use the perceptually significant SSIM index, which produces results largely in agreement with PSNR-B. Simulation results show that the PSNR-B results in better performance for quality assessment of deblocked images than PSNR and a well-known blockings-specific index.
BLOCKING effects are common in block-based image and video compression systems. Blocking artifacts are more serious at low bit rates, where network bandwidths are limited. Significant research has been done on blocking artifact reduction. Most blocking artifact reduction methods assume that the distorted image contains noticeable amount of blocking. The degree of blocking depends upon several parameters, the most important of which is the quantization step for lossy compression. Little research has done on comparing the perceptual quality of deblocked images. The recent advent of powerful modern image quality assessment (IQA) algorithms that compare well with human subjectively makes this plausible. Here we investigate quality assessment of deblocked images, and in particular we study the effects of the quantization step of the measured quality of deblocked images. A deblocking filter can improve image quality in some aspects, but can reduce image quality in other regards. We perform simulations on the quality assessment of deblocked images. We first perform simulations using the conventional peak signal-to-noise ratio (PSNR) quality metric and a state of the art quality index, the structural similarity (SSIM) index. The PSNR does not capture subjective quality well when blocking artifacts are present. The SSIM metric is slightlymore complex than the PSNR, but correlates highly with human subjectively.