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

Nowadays, 360° video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing. In this article, we review the state-of-the-art works on 360° video/image processing from the aspects of perception, assessment and compression. First, this article reviews both datasets and visual attention modelling approaches for 360° video/image. Second, we survey the related works on both subjective and objective visual quality assessment (VQA) of 360° video/image. Third, we overview the compression approaches for 360° video/image, which either utilize the spherical characteristics or visual attention models. Finally, we summarize this overview article and outlook the future research trends on 360° video/image processing.

State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression

In contrast with traditional videos, omnidirectional videos enable spherical viewing direction with support for head-mounted displays, providing an interactive and immersive experience Unfortunately, to the best of our knowledge, there are only a few visual quality assessment (VQA) methods, either subjective or objective, for omnidirectional video coding This paper proposes both subjective and objective methods for assessing the quality loss in encoding an omnidirectional video Specifically, we first present a new database, which includes the viewing direction data from several subjects watching omnidirectional video sequences Then, from our database, we find a high consistency in viewing directions across different subjects The viewing directions are normally distributed in the center of the front regions, but they sometimes fall into other regions, related to the video content Given this finding, we present a subjective VQA method for measuring the difference mean opinion score (DMOS) of the whole and regional omnidirectional video, in terms of overall DMOS and vectorized DMOS, respectively Moreover, we propose two objective VQA methods for the encoded omnidirectional video, in light of the human perception characteristics of the omnidirectional video One method weighs the distortion of pixels with regard to their distances to the center of front regions, which considers human preference in a panorama The other method predicts viewing directions according to the video content, and then the predicted viewing directions are leveraged to allocate weights to the distortion of each pixel in our objective VQA method Finally, our experimental results verify that both the subjective and objective methods proposed in this paper advance the state-of-the-art VQA for omnidirectional videos

Assessing Visual Quality of Omnidirectional Videos

In this paper, we propose a novel deep learning-based virtual reality image quality assessment method that automatically predicts the visual quality of an omnidirectional image. In order to assess the visual quality in viewing the omnidirectional image, we propose deep networks consisting of virtual reality (VR) quality score predictor and human perception guider. The proposed VR quality score predictor learns the positional and visual characteristics of the omnidirectional image by encoding the positional feature and visual feature of a patch on the omnidirectional image. With the encoded positional feature and visual feature, patch weight and patch quality score are estimated. Then, by aggregating all weights and scores of the patches, the image quality score is predicted. The proposed human perception guider evaluates the predicted quality score by referring to the human subjective score (i.e., ground-truth obtained by subjects) using an adversarial learning. With adversarial learning, the VR quality score predictor is trained to accurately predict the quality score in order to deceive the guider, while the proposed human perception guider is trained to precisely distinguish between the predictor score and the ground-truth subjective score. To verify the performance of the proposed method, we conducted comprehensive subjective experiments and evaluated the performance of the proposed method. The experimental results show that the proposed method outperforms the existing two-dimentional image quality models and the state-of-the-art image quality models for omnidirectional images.

Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image

Estimating salient areas of visual stimuli which are liable to attract viewers’ visual attention is a challenging task because of the high complexity of cognitive behaviors in the brain. Many researchers have been dedicated to this field and obtained many achievements. Some application areas, ranging from computer vision, computer graphics, to multimedia processing, can benefit from saliency detection, considering that the detected saliency has depicted the visual importance of different areas of the visual stimuli. As for the 360 degree visual stimuli, images and videos should record the whole scene in the 3D world, so the resolutions of panoramic images and videos are usually very high. However, when watching 360 degree stimuli, observers can only see part of the scene in the view port, which is presented to the eyes of the observers through the Head Mounted Display (HMD). So sending the whole video, or rendering the whole scene may result in the waste of resources. Thus if we can predict the current field of view, then focuses can be put to the streaming and rendering of the scene in the current field of view. Further more, if we can predict salient areas in the scene, then more fine processing can be done to the visually important areas. The prediction of salient regions for traditional images and videos have been extensively studied. However, conventional saliency prediction methods are not fully adequate for 360 degree contents, because 360 degree stimuli own some unique characteristics. Related study in this area is limited. In this paper, we study the problem of predicting head movement, head–eye motion, and scanpath of viewers when they are watching 360 degree images in the commodity HMDs. Three types of data are specifically analyzed. The first is the head movement data, which can be regarded as the movement of the view port. The second is the head–eye motion data which combines the motion of the head and the movement of the eye within the view port. The third is the scan-paths data of observers in the entire panorama which record the position information as well as the time information. And our model is designed to predict the saliency maps for the first two, and the scanpaths for the last one. Experimental results demonstrate the effectiveness of our model.

The prediction of head and eye movement for 360 degree images

Recent advances in omnidirectional video coding for virtual reality: Projection and evaluation

In this paper, a model using color dictionary based sparse representation for 360° image saliency prediction is proposed, referred to as CDSR. The proposed model simulates human color perception, extracting the image features by color dictionary based sparse representation, combining with weighted center–surround differences between image patches. Additionally, the partitioning operation, and latitude-bias enhancement are integrated into the proposed model to adapt for 360° image saliency prediction. Experimental results on both natural images and 360° images show the superior performances of the proposed saliency prediction model.

A saliency prediction model on 360 degree images using color dictionary based sparse representation

Objective quality assessment plays a crucial role in the evaluation and optimization processes of Virtual Reality (VR) technologies, for which state-of-the-art objective quality evaluation metrics for omnidirectional video, i.e., 360 degree video, are typically derived from traditional MSE (or PSNR). Here we propose an objective omnidirectional video quality assessment method based on structural similarity (SSIM) in the spherical domain. Adopting the relationship of the structural similarity between the 2-D plane and sphere, the interference brought by the projection between the two domains can be well handled in the assessment process. The performance of the proposed spherical structural similarity (S-SSIM) index is evaluated with a subjective omnidirectional video quality assessment database. As demonstrated in the experimental results, the proposed S-SSIM outperforms state-of-the-art objective quality assessment metrics in omnidirectional video quality assessment.

Spherical Structural Similarity Index for Objective Omnidirectional Video Quality Assessment

With the development of virtual reality, higher quality panoramic videos are in great demand to guarantee the immersive viewing experience. Therefore, quality assessment attaches much importance to correlated technologies. Considering the geometric transformation in projection and the limited resolution of head-mounted device (HMD), a modified display protocol of the high resolution sequences for the subjective rating test is proposed, in which an optimal display resolution is determined based on the geometry constraints between screen and human eyes. By sampling the videos to the optimal resolution before coding, the proposed method significantly alleviates the interference of HMD sampling while displaying, thus ensuring the reliability of subjective quality opinion in terms of video coding. Using the proposed display protocol, a subjective quality database for panoramic videos is established for video coding applications. The proposed database contains 50 distorted sequences obtained from ten raw panoramic video sequences. Distortions are introduced with the High Efficiency Video Coding compression. Each sequence is evaluated by 30 subjects on video quality, following the absolute category rating with hidden reference method. The rating scores and differential mean opinion scores (DMOSs) are recorded and included in the database. With the proposed database, several state-of-the-art objective quality assessment methods are further evaluated with correlation analysis. The database, including the video sequences, subjective rating scores and DMOS, can be used to facilitate future researches on coding applications.

Subjective Panoramic Video Quality Assessment Database for Coding Applications

Image compression is essential for remote sensing due to the large volume of produced remote sensing imagery and system’s limited transmission or storage capacity. As one of the most important applications, classification might be affected due to the introduced distortion during compression. Hence, we perform a quantitative study on the effects of compression on remote sensing image classification and propose a method to estimate the remote sensing image classification accuracy based on fractal analysis. Multiscale feature extraction is performed and a multiple kernel learning approach is proposed accordingly. The experimental results on our established database indicate that the classification accuracy predicted by our method exhibits high consistency with the ground truth and our method shows its superiority when compared with other classical reference algorithms.

Yingxue Zhang

Papers

Recent advances in omnidirectional video coding for virtual reality: Projection and evaluation

A saliency prediction model on 360 degree images using color dictionary based sparse representation

Spherical Structural Similarity Index for Objective Omnidirectional Video Quality Assessment

Subjective Panoramic Video Quality Assessment Database for Coding Applications

Effects of Compression on Remote Sensing Image Classification Based on Fractal Analysis