Perceptual quality assessmentplays a vital role in the visual communication systems owing to theexistence of quality degradations introduced in various stages of visual signalacquisition, compression, transmission and display.Quality assessment for visual signals can be performed subjectively andobjectively, and objective quality assessment is usually preferred owing to itshigh efficiency and easy deployment. A large number of subjective andobjective visual quality assessment studies have been conducted during recent years.In this survey, we give an up-to-date and comprehensivereview of these studies.Specifically, the frequently used subjective image quality assessment databases are firstreviewed, as they serve as the validation set for the objective measures.Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures.Third, the performances of the state-of-the-artquality measures for visual signals are compared with an introduction of theevaluation protocols.This survey provides a general overview of classical algorithms andrecent progresses in the field of perceptual image quality assessment.

Perceptual image quality assessment: a survey

In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods.

No Reference Quality Assessment for Screen Content Images With Both Local and Global Feature Representation

In this paper, we propose to use the deep learning technique for abnormal event detection by extracting spatiotemporal features from video sequences. Human eyes are often attracted to abnormal events in video sequences, thus we firstly extract saliency information (SI) of video frames as the feature representation in the spatial domain. Optical flow (OF) is estimated as an important feature of video sequences in the temporal domain. To extract the accurate motion information, multi-scale histogram optical flow (MHOF) can be obtained through OF. We combine MHOF and SI into the spatiotemporal features of video frames. Finally a deep learning network, PCANet, is adopted to extract high-level features for abnormal event detection. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones on the public video database.

Abnormal event detection in crowded scenes based on deep learning

We present a survey of transport methods for 3-D video ranging from early analog 3DTV systems to most recent digital technologies that show promise in designing 3DTV systems of tomorrow. Potential digital transport architectures for 3DTV include the DVB architecture for broadcast and the Internet Protocol (IP) architecture for wired or wireless streaming. There are different multiview representation/compression methods for delivering the 3-D experience, which provide a tradeoff between compression efficiency, random access to views, and ease of rate adaptation, including the "video-plus-depth" compressed representation and various multiview video coding (MVC) options. Commercial activities using these representations in broadcast and IP streaming have emerged, and successful transport of such data has been reported. Motivated by the growing impact of the Internet protocol based media transport technologies, we focus on the ubiquitous Internet as the network infrastructure of choice for future 3DTV systems. Current research issues in unicast and multicast mode multiview video streaming include network protocols such as DCCP and peer-to-peer protocols, effective congestion control, packet loss protection and concealment, video rate adaptation, and network/service scalability. Examples of end-to-end systems for multiview video streaming have been provided.

Transport Methods in 3DTV—A Survey

Stereoscopic image quality assessment method based on binocular combination saliency model

Blind quality assessment of screen content images (SCIs) is much challenging than traditional natural images. In this paper, we propose a blind quality predictor for SCIs to explore the issue from the perspective of sparse representation. Specifically, we conduct local sparse representation for the textual and pictorial regions, respectively, and conduct global sparse representation for the global SCIs. Subsequently, the underlying relationship between the feature and quality vectors is bridged in a universal sparse representation framework. The quality pooling is comparatively simply that only need to estimate the local and global quality scores and combine them to a total one. Our experimental results show that the proposed predictor can achieve better prediction performance to be in line with subjective assessment.

Toward a Blind Quality Predictor for Screen Content Images

The invention discloses a stereo image quality objective evaluation method, which comprises the following steps of: performing singular value decomposition on left and right viewpoint images of undistorted stereo images and distorted stereo images, extracting characteristic vectors of the left and right viewpoint images of the distorted stereo images, and performing linear weighting on the characteristic vectors of the left and right viewpoint images to obtain the characteristic vectors of the distorted stereo images; and training the characteristic vectors of the distorted stereo images of the same distortion type in a distorted stereo image set by using support vector regression, and testing each distorted stereo image of the same distortion type by utilizing a support vector regression training mode to obtain image quality objective evaluation predicted values of each distorted stereo image. The method has the advantages of mapping raw data into a high-dimensional characteristic space and performing linear estimation in the high-dimensional characteristic space to construct an optimal linear function so as to avoid the complex process of simulating the related characteristics and mechanism of a human vision system and improve correlation between objective evaluation results and subjective perception.

Stereo image quality objective evaluation method

The invention discloses a stereo image quality objective evaluation method based on visual perception. Firstly, a stereo image is divided into a strong edge block, a weak edge block , a flat block and a texture block, and characteristic information reflecting image quality and depth perception of different zone blocks is extracted through computation so as to obtain stereo image characteristic vectors; and then the characteristic vectors of distorted stereo images of the same distortion type in a distorted stereo image set are trained through support vector regression, and each distorted stereo image of the same distortion type is tested by a support vector regression training model to obtain the objective image quality evaluation forecast value of each distorted stereo image. The method has the advantages that the obtained characteristic vector information reflecting the image quality and the depth perception has stronger stability and can better reflect the quality change condition of the stereo images, and the relevance of an objective evaluation result and subjective perception is improved.

Fucui Li

Papers

Toward a Blind Quality Predictor for Screen Content Images

Stereo image quality objective evaluation method

Stereo image quality objective evaluation method based on visual perception

Binocular energy response based quality assessment of stereoscopic images

Local and global sparse representation for no-reference quality assessment of stereoscopic images