Encoded bitstream based video content type definition for HEVC video quality prediction

Abstract

In the past, many video quality assessment methods have been proposed to predict video quality non-intrusively from different application and/or network related parameters. One of the important parameters which have been identified to have a significant impact on video quality is the video content type. To determine video content type, researchers have used different methods such as the extraction of video motion features from the encoded bitstream, the calculation of pixel-wise differences between consecutive frames and human vision systems. In this paper, we developed a new motion amount (MA) metric for defining and determining video content type for the newly released HEVC encoding standard. This metric is based on a combination of the counting number of extracted motion vectors from an encoded bitstream, the magnitude and the complexity of video sequences. Accuracy of the metric was determined by performing a direct correlation between MA, video bitrate and Peak Signal-to-Noise Ratio (PSNR). We also performed a performance comparison with existing pixel-wise approach of defining video content type. Preliminary results show that our metric outperformed the pixel-wise approach of content type definition. Based on the new metric, we further developed an HEVC encoded video quality prediction model that takes into account the MA and the Quantization Parameter (QP). We achieved an accuracy of 96% when our model predicted PSNR values are compared with full reference PSNR measurement.