PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Quality-of-Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subject-labeled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. Based on the PEA265 database, we develop and optimize Convolutional Neural Networks (CNNs) to objectively recognize different types of PEAs. Experiments show that our architecture is capable of identifying the 6 types of PEAs with an accuracy over 86%. To further demonstrate its application, we explore the relationship between collected PEA intensities and subjective quality scores of compressed videos. A quality metric is consequently proposed with superior performance in terms of correlation to Mean Opinion Score (MOS) values. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.