Abstract
Developing an objective video quality metric that accurately estimates perceived video quality is challenging. Developing a metric that can additionally be embedded in the rate distortion optimization process of a video codec can be even harder given that decisions have to be made locally. In this paper, we present a method for combining a number of existing state of the art objective video quality metrics at the coding block level by employing a fusion of local content features for deciding how to best utilize the chosen metrics. Our results indicate promising performance in terms of the correlation of the developed locally-acting quality metric with the overall perceived quality of the video.
Highlights
Video quality has been traditionally evaluated using Mean Squared Error (MSE), it is already known that it does not linearly correlate with the perceived quality due to the human visual system properties that are not captured by it [1]
These metrics were either initially designed for images, such as the Structural Similarity Index (SSIM) [1], Peak Signal to Noise Ratio based on HVS (PSNRHVSM) [3], Multi-Scale SSIM (MS-SSIM) [4], Visual Information Fidelity (VIF) [5], Feature Similarity Index (FSIM) [6]; or for video, such as Perception-based Video Metric (PVM) [7], Motion-based Video Integrity Evaluation (MOVIE) index [8], or Video Quality Metric (VQM) [9]
For the BVQA model validation, we evaluate the performance of the method against the best performing metrics from Table 3, namely FSIM, MS-SSIM, and PSNR-HVSM
Summary
Video quality has been traditionally evaluated using Mean Squared Error (MSE), it is already known that it does not linearly correlate with the perceived quality due to the human visual system properties that are not captured by it [1]. It is important to note that improving PSNR [16] by adapting it to subjective quality evaluation scores has received extensive research Choosing amongst all these metrics is a challenge by itself as they each offer different levels of performance for different content. Being trained on a large varied dataset, VMAF shows higher correlation to subjective quality compared to other objective quality metrics It evaluates the overall frame quality, which is not ideal in an RDO environment where block-level quality estimation is required. This work introduces a block-level fusion of objective metrics for video quality assessment (BVQA).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
