MC360IQA: A Multi-channel CNN for Blind 360-Degree Image Quality Assessment

Sun, Weiqiang; Min, Xiongkuo; Zhai, Guangtao; Gu, Ke; Duan, Huiyu; Ma, Siwei

doi:10.1109/jstsp.2019.2955024

Cited by 133 publications

(64 citation statements)

References 64 publications

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“…Although FR and RR IQA [5] usually perform well, the reference image is not available in most cases, especially for low-light images, which is why we focus on the NR IQA in this paper. In the past two decades, a series of NR IQA methods [6] [7] have been proposed. For example, BRISQUE [8] assesses image quality based on scene statistics of locally normalized luminance coefficients.…”

Section: Introductionmentioning

confidence: 99%

A No-Reference Evaluation Metric for Low-Light Image Enhancement

Zhang

Sun

Min

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Low-light images, which are usually taken in dark or backlighting conditions, are hard to perceive due to the low visibility and low contrast. To improve viewers' Quality of Experience (QoE) and support the application of vision-based systems, various low-light image enhancement algorithms (LIEAs) have been proposed to lighten low-light images. However, some LIEAs may amplify the hidden distortions in the dark like noise and even further, introduce new distortions such as structural damage, color shift, etc, which severely affect the quality of light-enhanced images and need to be evaluated quantificationally. However, in the literature, few measures are proposed to assess the quality of light-enhanced images. Therefore, in this paper, we develop a no-reference lowlight image enhancement evaluation (NLIEE) metric to predict the quality of light-enhanced images. The image quality is mainly assessed from four key aspects: light enhancement, color comparison, noise measurement, and structure evaluation. The experiment results show that NLIEE achieves the best performance among the general no-reference image quality assessment (NR IQA) models and quality descriptors for light enhancement.

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Section: Introductionmentioning

confidence: 99%

A No-Reference Evaluation Metric for Low-Light Image Enhancement

Zhang

Sun

Min

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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“…Recently, data-driven approaches have gained popularity in NR-IQA that do not rely on NSS-based or other hand-crafted features [31,32]. The work of Lv et al [33] is a transition between hand-crafted features based and deep learning based approaches.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images

Varga

2021

Electronics

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With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images in various viewpoints. Benford’s law makes a prediction for the probability distribution of first digits in natural datasets. It has been applied among others for detecting fraudulent income tax returns, detecting scientific fraud, election forensics, and image forensics. In particular, our analysis is based on first digit distributions in multiple domains (wavelet coefficients, DCT coefficients, singular values, etc.) as feature vectors and the extracted features are mapped onto image quality scores. Extensive experiments have been carried out on seven large image quality benchmark databases. It has been demonstrated that first digit distributions are quality-aware features, and it is possible to reach or outperform the state-of-the-art with them.

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“…They suggested a testbed for conducting subjective tests on an omnidirectional environment using different projection schemes. Sun et al [8] proposed a novel image quality assessment model for non-reference 360 images that were compared against other existing image quality assessment models on 360 databases. Narciso et al [9] proposed a study using an HMD to measure the influence of video format and sound format on a user's sense of presence and cybersickness.…”

Section: Introductionmentioning

confidence: 99%

Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset

et al. 2021

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The use of Stereoscopic 3D (S3D) videos has been popular in commercial markets with ongoing developments in the field of visual entertainment in recent years. A wide variety of projection methods of 3D video content is currently available, such as projection to a panoramic screen and projection of omnidirectional video content from head mounted displays using Virtual Reality (VR) technology. This paper investigates the Quality of Experience (QoE) and associated Visually Induced Motion Sickness (VIMS) caused by the viewing of S3D videos. The investigations used three different projection screens: a 3D flat screen, a 3D panoramic screen in a hemispherical shaped room and a VR headset. Several assessment methods including a Simulator Sickness Questionnaire (SSQ), ElectroEncephaloGraphy (EEG), and measurement tools for eye blink rate detection were applied to measure the QoE experienced by viewers. The SSQ scores were also compared with the behavioral data such as attention and meditation levels and enjoyment ratings acquired from different video content and projection screens. The results indicate that the projection screen is a key factor affecting the level of visual fatigue, VIMS and QoE assessments, which are discussed in-depth in the paper.

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MC360IQA: A Multi-channel CNN for Blind 360-Degree Image Quality Assessment

Cited by 133 publications

References 64 publications

A No-Reference Evaluation Metric for Low-Light Image Enhancement

A No-Reference Evaluation Metric for Low-Light Image Enhancement

Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images

Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset

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