Visual Quality and Perceptual Coding

Author: Hyunsuk Ko and C.-C. Jay Kuo

A new framework for objective quality evaluation of stereoscopic image pairs is the goal of this research. Quality assessment of stereoscopic image pairs is more complicated than that for 2D images since it is a multi-dimensional problem where the quality is affected by distortion types as well as the relation between the left and right views (e.g. different levels of distortion in two views). In this work, a new formula-based metric was introduced to provide results better than state-of-the art methods. However, the formula-based metric still has its limitation. For further improvement, we further propose a 2-stage fusion model. That is, we classify distortion types into groups and design a set of scorer to handle them separately. In Stage 1, each scorer generates its own score. In Stage 2, all intermediate scores are fused to predict the final quality index with nonlinear regression. Experimental results demonstrate that the proposed quality index outperforms several existing quality assessment methods by a significant margin over different databases.

Although research on advanced image/video quality index methods that are more consistent with the human visual experience has made a substantial amount of progress in the last decade, the study on 3D image/video quality is still in its early stage. The 3D quality assessment (QA) is a difficult problem since it is affected by 2D image quality, depth perception and visual discomfort such as eye strain or dizziness. It is particularly challenging when the stereoscopic image pair consists of two views with different quality levels (called asymmetric distortion). To address this problem, we need a deeper understanding of the human visual system (HVS), e.g. the binocular combination in stereovision, to build a robust index that [...]

Author: Tsung-Jung Liu, Weisi Lin, and C.-C. Jay Kuo

A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called “context-dependent MMF” (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.

Although the proposed MMF has excellent performance, one issue concerning context classification for CD-MMF needs to be resolved in the future. Since one image may consist of multiple distortion types, the strict classification of images into one specific context may lead to the wrong context category, and then affect the subsequent quality prediction. One possible and better solution to overcome this shortcoming is to use unsupervised classification for context determination. Another alternative is to attach beliefs to the classification of the context and weight the corresponding regressed predicted [...]