A Novel No-Reference PSNR Estimation Method With Regard to Deblocking Filtering Effect in H.264/AVC Bitstreams

Peak signal-to-noise ratio (PSNR) monitoring is an important application for video quality assessment of video systems at the receiver sides where no-reference PSNR estimation is essential. Most of the PSNR estimation methods for H.264/AVC bitstreams ignore or do not consider the effect of deblocking filtering. Instead, they only focus on estimating the mean squared error (MSE) due to quantization. However, the PSNR estimation affected by deblocking filtering cannot be negligible for sequences of large picture resolutions. In this paper, we first present an MSE estimation method on H.264/AVC bitstreams by considering the deblocking filtering effect so that more accurate PSNR estimation can be made. For this, the total MSE between the original and reconstructed frames is separated into two terms for PSNR estimation: one due to quantization error and the other due to the deblocking filtering effect in H.264/AVC. In the proposed PSNR estimation, the contribution of deblocking filtering to the total MSE is quantified by a compensation factor of each encoded picture type between the original and the deblocked frames. Experimental results show that the proposed method effectively reflects the contribution of deblocking filtering to PSNR estimation, thus yielding more accurate PSNR estimates.

I. Introduction

With the rapid increase in the demand for high-quality video services, quality of service (QoS) has become an important issue; thus, intensive study of accurate quality assessment methods on video is essential. The Video Quality Expert Group (VQEG) has specified several guidelines to measure video quality [1], [2]. Traditionally, quality assessment methods are classified into three kinds, depending on the availability of original reference data: 1) full-reference (FR) methods measure the quality by referencing the original data [3], [4]; 2) reduced-reference (RR) methods [5] are similar to the full-reference except that the original data are limitedly transmitted or partially used at receiver sides; and 3) no-reference (NR) methods make visual quality assessment (VQA) without the original data, which can be very practical in many applications [6].