A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images

Mahmoudpour, Saeed; Schelkens, Peter

doi:10.1109/tmm.2019.2950570

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…There is no doubt that for intelligent vehicles, the perception of moving objects on the road is the premise of achieving safe autonomous driving, which mainly includes two tasks: the detection and recognition of moving objects. Dynamic object detection technology is very important and basic in the field of machine vision, which forms an important branch of machine vision [38,39]. After decades of development, many dynamic object detection methods using 2D images have been developed, based on which the subsequent researches mainly focused on the improvement of these methods to suit for different and more complex application scenarios by adding new mathematical methods.…”

Section: Multisensor Fusion/resultsmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

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Section: Multisensor Fusion/resultsmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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“…HDR-based BIQAs measure the quality degradation mainly caused by tone mapping or multiple exposure fusion. Some BIQAs have considered representative quality descriptors, such as gradient [34], color [50], and brightness [29]. With the continuous development of HDR, the use of new technologies (tone-mapping or exposure fusion algorithm) has improved HDR imaging results.…”

Section: Distortion-specific Biqasmentioning

confidence: 99%

Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images

Wang¹,

Xu²,

Xu³

et al. 2023

Preprint

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Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals, which has been widely used to monitor product and service quality in low-light applications, covering smartphone photography, video surveillance, autonomous driving, etc. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns, where human visual perception is simultaneously reflected by multiple sensory information (e.g., sight and hearing). In this article, we present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score. To investigate the multimodal mechanism, we first establish a multimodal low-light image quality (MLIQ) database with authentic low-light distortions, containing image and audio modality pairs. Further, we specially design the key modules of BMQA, considering multimodal quality representation, latent feature alignment and fusion, and hybrid self-supervised and supervised learning. Extensive experiments show that our BMQA yields state-of-the-art accuracy on the proposed MLIQ benchmark database. In particular, we also build an independent single-image modality Dark-4K database, which is used to verify its applicability and generalization performance in mainstream unimodal applications. Qualitative and quantitative results on Dark-4K show that BMQA achieves superior performance to existing BIQA approaches as long as a pre-trained quality semantic description model is provided. The proposed framework and two databases as well as the collected BIQA methods and evaluation metrics are made publicly available.

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“…Considering the multi-scale nature of human vision, we downsample night-time image and extract 32-dimensional local and global features from two scales. To ensure fairness in subsequent experiments, we use the LIBSVM package [22] to implement SVR, and adopt the radial basis function (RBF) kernel to build the quality assessment model as the competing methods do [5,6,11]. sets are non-overlapped.…”

Section: Image Quality Assessment Modelmentioning

confidence: 99%

No-Reference Quality Assessment of Night-Time Images Via the Analysis of Local and Global Features

Song

Hou

Yue

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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This paper focuses on the quality assessment of the nighttime images, which is technically vulnerable to diverse and sophisticated visual distortions, and proposes an effective noreference image quality assessment method through comprehensive analysis of local and global image attributes. Several quality-aware features are extracted through the study of image brightness, contrast, structure and color. Specifically, the features related to brightness and contrast are extracted through the analysis of local information, while the others are extracted through the analysis of global information. The quality assessment model for night-time images is built by mapping the selected features into subjective ratings via the support vector regression. Extensive experiments conducted on public night-time image database demonstrates the superiority of the proposed method.

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A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images

Cited by 8 publications

References 50 publications

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images

No-Reference Quality Assessment of Night-Time Images Via the Analysis of Local and Global Features

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