Low-Light Images In-the-Wild: A Novel Visibility Perception-Guided Blind Quality Indicator

Owing to the increasing deployment of CMOS camera modules, it is inevitable to take photographs under weak illumination. Therefore, low-light imaging quality is one of the most important factors affecting user experience as well as the product values of consumer electronics, automobile, surveillance, factory automation, and other industrial applications. Inspired by human vision, this article jointly considers visibility perception, luminosity cognition, and color sensation and presents a new visibility perception-guided blind quality indicator for low-light images in-the-wild. To excavate effective descriptors for authentic distortions under weak illumination, we utilize maximum ignorable visible difference to characterize the reduced visibility, and employ the luminance statistical properties and color sensation characteristics to represent brightness and colorfulness distortions. Extensive experimental results on the benchmark dataset verify that the proposed blind quality indicator outperforms nine representative methods including general-purpose and distortion-specific methods.

[1]  Sung Wook Baik,et al.  AI-Assisted Edge Vision for Violence Detection in IoT-Based Industrial Surveillance Networks , 2022, IEEE Transactions on Industrial Informatics.

[2]  Sharath Chandra Guntuku,et al.  Perceptual Quality Evaluation of Hazy Natural Images , 2021, IEEE Transactions on Industrial Informatics.

[3]  Changyun Wen,et al.  SCENS: Simultaneous Contrast Enhancement and Noise Suppression for Low-Light Images , 2021, IEEE Transactions on Industrial Electronics.

[4]  Hong Li,et al.  No-Reference Image Contrast Evaluation by Generating Bidirectional Pseudoreferences , 2021, IEEE Transactions on Industrial Informatics.

[5]  Miaohui Wang,et al.  Blind Quality Assessment of Night-Time Images Via Weak Illumination Analysis , 2021, 2021 IEEE International Conference on Multimedia and Expo (ICME).

[6]  Wenying Wen,et al.  Perceptual Quality Assessment for Screen Content Images by Spatial Continuity , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Guang-Zhong Cao,et al.  Low-Illumination Image Enhancement for Night-Time UAV Pedestrian Detection , 2020, IEEE Transactions on Industrial Informatics.

[8]  Tao Xiang,et al.  Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches , 2020, IEEE Transactions on Multimedia.

[9]  Guangming Shi,et al.  MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Sam Kwong,et al.  Toward Accurate Quality Estimation of Screen Content Pictures With Very Sparse Reference Information , 2020, IEEE Transactions on Industrial Electronics.

[11]  Guanghui Yue,et al.  Referenceless Quality Evaluation of Tone-Mapped HDR and Multiexposure Fused Images , 2020, IEEE Transactions on Industrial Informatics.

[12]  Anirban Dasgupta,et al.  A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers , 2019, IEEE Transactions on Intelligent Transportation Systems.

[13]  Zhou Wang,et al.  A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms , 2019, IEEE Access.

[14]  Zhibo Chen,et al.  Tensor Oriented No-Reference Light Field Image Quality Assessment , 2019, IEEE Transactions on Image Processing.

[15]  Weisi Lin,et al.  A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection , 2019, IEEE Transactions on Industrial Informatics.

[16]  Jongyoo Kim,et al.  Deep CNN-Based Blind Image Quality Predictor , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Damon M. Chandler,et al.  Opinion-Unaware Blind Quality Assessment of Multiply and Singly Distorted Images via Distortion Parameter Estimation , 2018, IEEE Transactions on Image Processing.

[18]  Yibing Zhan,et al.  No-Reference Image Sharpness Assessment Based on Maximum Gradient and Variability of Gradients , 2018, IEEE Transactions on Multimedia.

[19]  Martijn Wisse,et al.  Integrating Different Levels of Automation: Lessons From Winning the Amazon Robotics Challenge 2016 , 2018, IEEE Transactions on Industrial Informatics.

[20]  Wenjun Zhang,et al.  No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization , 2017, IEEE Transactions on Cybernetics.

[21]  Guangming Shi,et al.  Enhanced Just Noticeable Difference Model for Images With Pattern Complexity , 2017, IEEE Transactions on Image Processing.

[22]  Sangkeun Lee,et al.  Artifact-Free Low-Light Video Enhancement Using Temporal Similarity and Guide Map , 2017, IEEE Transactions on Industrial Electronics.

[23]  Sebastian Bosse,et al.  Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment , 2016, IEEE Transactions on Image Processing.

[24]  Weisi Lin,et al.  No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain , 2016, IEEE Signal Processing Letters.

[25]  Lei Zhang,et al.  A Feature-Enriched Completely Blind Image Quality Evaluator , 2015, IEEE Transactions on Image Processing.

[26]  Ling Shao,et al.  Non-distortion-specific no-reference image quality assessment: A survey , 2015, Inf. Sci..

[27]  Lei Zhang,et al.  Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features , 2014, IEEE Transactions on Image Processing.

[28]  Hua Huang,et al.  No-reference image quality assessment based on spatial and spectral entropies , 2014, Signal Process. Image Commun..

[29]  Damon M. Chandler,et al.  No-Reference Quality Assessment of JPEG Images via a Quality Relevance Map , 2014, IEEE Signal Processing Letters.

[30]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[31]  A. Roli Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[32]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[33]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[34]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[36]  Wen Gao,et al.  Blind Quality Assessment of Camera Images Based on Low-Level and High-Level Statistical Features , 2019, IEEE Transactions on Multimedia.

[37]  Zhou Wang,et al.  Applications of Objective Image Quality Assessment Methods , 2011 .

[38]  D. Ruderman The statistics of natural images , 1994 .