A reduced-reference perceptual image and video quality metric based on edge preservation

In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al

A Matlab-Based Tool for Video Quality Evaluation without Reference

This paper deals with the design of a Matlab based tool for measuring video quality with no use of a reference sequence. The main goals are described and the tool and its features are shown. The paper begins with a description of the existing pixel-based no-reference quality metrics. Then, a novel algorithm for simple PSNR estimation of H.264/AVC coded videos is presented as an alternative. The algorithm was designed and tested using publicly available video database of H.264/AVC coded videos. Cross-validation was used to confirm the consistency of results

Image Quality Assessment Based on Detail Differences

This paper presents a novel Full Reference method for image quality assessment based on two indices measuring respectively detail loss and spurious detail addition. These indices define a two dimensional (2D) state in a Virtual Cognitive State (VCS) space. The quality estimation is obtained as a 2D function of the VCS, empirically determined via polynomial fitting of DMOS values of training images. The method provides at the same time highly accurate DMOS estimates, and a quantitative account of the causes of quality degradation