Video decoder monitoring using non-linear regression

Abstract

In this research work, a non-linear regression-based prediction method is incorporated into a digital video decoder loop to monitor the visual quality of videos during the decoding process. Considering well-known video quality metrics, a Video Quality Monitoring Tool (VQMT) has been developed for efficient re-use in a variety of video processing tasks. The idea is based on the fact that when human observers rate video quality, they consider reference aspects such as Noise affecting the video or Neatness of images. In addition, transmission errors such as packet loss rate may impact video quality as well. Therefore, defining a Regression model between each one of these reference aspects and the Mean Opinion Score (MOS) provided by human observers can lead to an automatic way to supervise video decoding quality. Promising results have been achieved using a Non-linear Regression (NLR) method together with fundamental video quality metrics namely PLR (Packet Loss Rate), PSNR (Peak Signal to Noise Ratio), the SI (Spatial Index) and the TI (Temporal Index).