On the Improvement of 2D Quality Assessment Metrics for Omnidirectional Images

2020 ◽  
Vol 2020 (9) ◽  
pp. 287-1-287-11
Author(s):  
Abderrezzaq Sendjasni ◽  
Mohamed-Chaker Larabi ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Subjective Evaluation ◽  
Video Quality ◽  
Visual Saliency ◽  
Quality Metrics ◽  
Subjective Quality Assessment ◽  
Evaluation Approach ◽  
Omnidirectional Images

Subjective quality assessment remains the most reliable way to evaluate image quality while being tedious and money consuming. Therefore, objective quality evaluation ensures a trade-off by providing a computational approach for predicting image quality. Even though a large literature exists for 2D image and video quality evaluation, 360-degree images quality is still under-explored. One can question the efficiency of 2D quality metrics on such a new type of content. To this end, we propose to study the possible improvement of well-known 2D quality metrics using important features related to 360-degree content, i.e. equator bias and visual saliency. The performance evaluation is conducted on two databases containing various distortion types. The obtained results show a slight improvement of the performance highlighting some problems inherently related to both the database content and the subjective evaluation approach used to obtain the observers’ quality scores.

scholarly journals Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ruizhe Deng ◽  
Yang Zhao ◽  
Yong Ding
Keyword(s):  
Feature Extraction ◽  
Visual Cortex ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Subjective Evaluation ◽  
Visual Saliency ◽  
Perceptual Quality ◽  
Subjective Rating ◽  
Support Vector

Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, images are enhanced before feature extraction with the assistance of visual saliency maps since visual attention affects human evaluation of image quality. The similarities between the features extracted from distorted image and corresponding reference images are synthesized and mapped into an objective quality score by support vector regression. Experiments conducted on four public IQA databases show that the proposed method outperforms other state-of-the-art methods in terms of both accuracy and robustness; that is, it is highly consistent with subjective evaluation and is robust across different databases.

scholarly journals On the Effect of Standing and Seated Viewing of 360∘ Videos on Subjective Quality Assessment: A Pilot Study

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 80
Author(s):  
Yan Hu ◽  
Majed Elwardy ◽  
Hans-Jürgen Zepernick
Keyword(s):  
Pilot Study ◽  
Quality Assessment ◽  
Video Quality ◽  
Ground Truth ◽  
Subjective Quality ◽  
Simulator Sickness ◽  
Subjective Quality Assessment ◽  
Assessment Task ◽  
Average Rating ◽  
Subjective Tests

Due to the advances in head-mounted displays (HMDs), hardware and software technologies, and mobile connectivity, virtual reality (VR) applications such as viewing 360∘ videos on HMDs have seen an increased interest in a wide range of consumer and vertical markets. Quality assessment of digital media systems and services related to immersive visual stimuli has been one of the challenging problems of multimedia signal processing. Specifically, subjective quality assessment of 360∘ videos presented on HMDs is needed to obtain a ground truth on the visual quality as perceived by humans. Standardized test methodologies to assess the subjective quality of 360∘ videos on HMDs are currently not as developed as for conventional videos and are subject to further study. In addition, subjective tests related to quality assessment of 360∘ videos are commonly conducted with participants seated on a chair but neglect other options of consumption such as standing viewing. In this paper, we compare the effect that standing and seated viewing of 360∘ videos on an HMD has on subjective quality assessment. A pilot study was conducted to obtain psychophysical and psychophysiological data that covers explicit and implicit responses of the participants to the shown 360∘ video stimuli with different quality levels. The statistical analysis of the data gathered in the pilot study is reported in terms of average rating times, mean opinion scores, standard deviation of opinion scores, head movements, pupil diameter, galvanic skin response (GSR), and simulator sickness scores. The results indicate that the average rating times consumed for 360∘ video quality assessment are similar for standing and seated viewing. Further, the participants showed higher resolving power among different 360∘ video quality levels and were more confident about the given opinion scores for seated viewing. On the other hand, a larger scene exploration of 360∘ videos was observed for standing viewing which appears to distract from the quality assessment task. A slightly higher pupil dilation was recorded for standing viewing which suggests a slightly more immersed experience compared to seated viewing. GSR data indicate a lower degree of emotional arousal in seated viewing which seems to allow the participants to better conduct the quality assessment task. Similarly, simulator sickness symptoms are kept significantly lower when seated. The pilot study also contributes to a holistic view of subjective quality assessment and provides indicative ground truth that can guide the design of large-scale subjective tests.

Precise No-Reference Image Quality Evaluation Based on Distortion Identification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Chenggang Yan ◽  
Tong Teng ◽  
Yutao Liu ◽  
Yongbing Zhang ◽  
Haoqian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Gaussian White Noise ◽  
The State ◽  
Reference Image

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.

Applications and New Research of Visual Saliency and Attention

2013 ◽  
Vol 333-335 ◽  
pp. 1171-1174
Author(s):  
Fan Hui ◽  
Ren Lu ◽  
Jin Jiang Li
Keyword(s):  
Visual Attention ◽  
Quality Evaluation ◽  
Saliency Detection ◽  
Video Quality ◽  
Visual Saliency ◽  
Attention Model ◽  
Detection Model ◽  
Detection Algorithms ◽  
New Research ◽  
Video Quality Evaluation

Drawing on the suvey of visual attention degree and its significance in psychology and physiology , in recent years, researchers have proposed a lot of visual attention model and algorithms, such as Itti model and many saliency detection algorithms. And in recent years, the researchers applied the visual attention of this technology in a lot of directions, such as a significant regional shifts and visual tracing detection model based on network loss, for video quality evaluation. This paper summarizes the various algorithms and its application of visual attention and its significance.

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