Image quality assessment in the low quality regime

Abstract

Traditionally, image quality estimators have been designed and optimized to operate over the entire quality range of images in a database, from very low quality to visually lossless. However, if quality estimation is limited to a smaller quality range, their performances drop dramatically, and many image applications only operate over such a smaller range. This paper is concerned with one such range, the low-quality regime, which is defined as the interval of perceived quality scores where there exists a linear relationship between the perceived quality scores and the perceived utility scores and exists at the low-quality end of image databases. Using this definition, this paper describes a subjective experiment to determine the low-quality regime for databases of distorted images that include perceived quality scores but not perceived utility scores, such as CSIQ and LIVE. The performances of several image utility and quality estimators are evaluated in the low-quality regime, indicating that utility estimators can be successfully applied to estimate perceived quality in this regime. Omission of the lowestfrequency image content is shown to be crucial to the performances of both kinds of estimators. Additionally, this paper establishes an upper-bound for the performances of quality estimators in the LQR, using a family of quality estimators based on VIF. The resulting optimal quality estimator indicates that estimating quality in the low-quality regime is robust to exact frequency pooling weights, and that near-optimal performance can be achieved by a variety of estimators providing that they substantially emphasize the appropriate frequency content.