General Image Quality Assessment Research Articles

Subjective and Objective Quality Assessment of Compressed Screen Content Videos

With the widespread of application scenarios such as remote office and cloud collaboration, Screen Content Video (SCV) and its processing which show different characteristics from Natural Scene Video (NSV) and its processing, are increasingly attracting researcher's attention. Among these processing techniques, quality evaluation plays an important role in various media processing systems. Despite extensive research on general Image Quality Assessment (IQA) and Video Quality Assessment (VQA), quality assessment of SCVs remains undeveloped. In particular, SCVs always suffer from compression degradations in all kinds of application scenarios. In this article, we first study subjective SCV quality assessment. Specifically, we first construct a Compressed Screen Content Video Quality (CSCVQ) database with 165 distorted SCVs compressed from 11 most common screen application scenarios using the H.264, HEVC and HEVC-SCC formats. Twenty subjects were recruited to participate in the subjective test on the CSCVQ database. Then we study objective SCV quality assessment and propose a SCV quality measure. We observe that localized protruding information such as curves and dots can be well captured by the local relative standard deviation which then can be used to measure the intra-frame quality. Base on this observation, we develop a MutiScale Relative Standard Deviation Similarity (MS-RSDS) model for SCV quality evaluation. In our model, the relative standard deviation similarity between the reference and distorted SCVs is measured from frame differences between two adjacent frames, which can capture the spatiotemporal distortions accurately. A multiscale strategy is also applied to strengthen the original single-scale model. Extensive experiments are performed to compare the proposed model with the most popular and state-of-the-art quality assessment models on the CSCVQ database. Experimental results show that our proposed MS-RSDS model which has relatively low computation complexity, outperforms other IQA/VQA models.

Saliency-induced reduced-reference quality index for natural scene and screen content images

Massive content composed of both natural scene and screen content has been generated with the increasing use of wireless computing and cloud computing, which call for general image quality assessment (IQA) measures working for both natural scene images (NSIs) and screen content images (SCIs). In this paper, we develop a saliency-induced reduced-reference (SIRR) IQA measure for both NSIs and SCIs. Image quality and visual saliency are two widely studied and closely related research topics. Traditionally, visual saliency is often used as a weighting map in the final pooling stage of IQA. Instead, we detect visual saliency as a quality feature since different types and levels of degradation can strongly influence saliency detection. Image quality is described by the similarity between two images’ saliency maps. In SIRR, saliency is detected through a binary image descriptor called “image signature”, which significantly reduces the reference data. We perform extensive experiments on five large-scale NSI quality assessment databases including LIVE, TID2008, CSIQ, LIVEMD, CID2013, as well as two recently constructed SCI QA databases, i.e., SIQAD and QACS. Experimental results show that SIRR is comparable to state-of-the-art full-reference and reduced-reference IQA measures in NSIs, and it can outperform most competitors in SCIs. The most important is that SIRR is a cross-content-type measure, which works efficiently for both NSIs and SCIs. The MATLAB source code of SIRR will be publicly available with this paper.

Perceptual Depth Quality in Distorted Stereoscopic Images.

Subjective and objective measurement of the perceptual quality of depth information in symmetrically and asymmetrically distorted stereoscopic images is a fundamentally important issue in stereoscopic 3D imaging that has not been deeply investigated. Here, we first carry out a subjective test following the traditional absolute category rating protocol widely used in general image quality assessment research. We find this approach problematic, because monocular cues and the spatial quality of images have strong impact on the depth quality scores given by subjects, making it difficult to single out the actual contributions of stereoscopic cues in depth perception. To overcome this problem, we carry out a novel subjective study where depth effect is synthesized at different depth levels before various types and levels of symmetric and asymmetric distortions are applied. Instead of following the traditional approach, we ask subjects to identify and label depth polarizations, and a depth perception difficulty index (DPDI) is developed based on the percentage of correct and incorrect subject judgements. We find this approach highly effective at quantifying depth perception induced by stereo cues and observe a number of interesting effects regarding image content dependency, distortion-type dependence, and the impact of symmetric versus asymmetric distortions. Furthermore, we propose a novel computational model for DPDI prediction. Our results show that the proposed model, without explicitly identifying image distortion types, leads to highly promising DPDI prediction performance. We believe that these are useful steps toward building a comprehensive understanding on 3D quality-of-experience of stereoscopic images.