Objective quality assessment method of stereo images

Yang, Jiachen; Hou, Chunping; Zhou, Yuan; Zhang, Zhuoyun; Guo, Jun

doi:10.1109/3dtv.2009.5069615

Cited by 29 publications

(6 citation statements)

References 4 publications

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“…Difference perception-based metrics assess the image quality based on the difference between the left and right views. The difference image was first utilized in the FR-SIQA index presented in [35], representing information differences between two views. Zhang et al [36] proposed a CNN-based blind SIQA index that considers the difference image to represent the depth and distortion of the stereoscopic image.…”

Section: Blind Siqa For Singly-distorted Imagementioning

confidence: 99%

“…Furthermore, disparities between stereo images can vary significantly for different baselines, depths, and resolutions [33,32]. This poses a great challenge to the binocular combination of stereoscopic vision [35,38], leading to the occurrence of binocular rivalry [6] and other unpredictable visual behaviors [25] occur during the process. Consequently, the mixture of multiple distortions causes the problem of binocular quality prediction more complex and challenging.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the scene discrepancy between the left and right views, previous works [35,36,16,37] have found that the difference image between the left and right views of the distorted stereoscopic image is not a reliable representation of the distortion. To resolve the negative influence of the inaccurate distortion representation on SIQA, the monocular model based on the registration-based distortion representation is constructed to represent the distortion in the stereoscopic image more accurately.…”

Section: Introductionmentioning

confidence: 99%

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Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Shi,

Guo,

Niu

et al. 2023

Communications in Information and Systems

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Multiply-distorted stereoscopic images are common in real-world applications. The mixture of multiple distortions results in complex binocular visual behavior of multiply-distorted stereoscopic images, making it challenging for existing blind singly-distorted stereoscopic image quality assessment (IQA) methods to obtain satisfactory results on multiply-distorted stereoscopic images. Because binocular rivalry caused by different distortions in the left and right views greatly influences the final stereoscopic image quality, we propose a registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image in this paper. First, we employ a registration-based distortion representation to characterize the distortion in the stereoscopic image. Then we represent the binocular rivalry by merging the left and right views into a cyclopean image. Considering that the color and intensity of pixels in the RGB image can better reflect the information of the distorted image, then a grayscale cyclopean image is further converted to the color binocular representation using tone mapping. Finally, a multiply-distorted stereoscopic IQA method based on a double-stream convolutional neural network is proposed. The two subnetworks are used to extract quality features from the registration-based distortion representation and color binocular representation, respectively. Experimental results demonstrate that the proposed model outperforms the state-of-theart models on the multiply-distorted stereoscopic image databases.

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Section: Blind Siqa For Singly-distorted Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Shi,

Guo,

Niu

et al. 2023

Communications in Information and Systems

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“…Image Quality Assessment (IQA) refers to the quantitative analysis of the content of an image, thus quantifying the degree of visual distortion of a distorted image. The relevant evaluation methods are generally divided into two types: subjective quality assessment (Mantiuk, Tomaszewska, and Mantiuk 2012) and objective quality assessment (Yang et al 2009). Among them, the objective quality assessment is divided into three categories according to the extent of information the algorithm needs from reference image (Wang and Bovik 2006): full reference quality assessment (FR-IQA) (Wang et al 2004), reduced-reference quality assessment (RR-IQA) (Rehman and Wang 2012) and no reference quality assessment (NR-IQA) (Mittal, Moorthy, and 2012).…”

Section: Introductionmentioning

confidence: 99%

Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning

Shi,

Gao,

Qin

2024

AAAI

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Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network (CNN) and Transformers based NR-IQA methods have achieved considerable progress. However, they still exhibit limited capability when facing unknown authentic distortion datasets. To further improve NR-IQA performance, in this paper, a novel supervised contrastive learning (SCL) and Transformer-based NR-IQA model SaTQA is proposed. We first train a model on a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels. To further extract distortion information from images, we propose a backbone network incorporating the Multi-Stream Block (MSB) by combining the CNN inductive bias and Transformer long-term dependence modeling capability. Finally, we propose the Patch Attention Block (PAB) to obtain the final distorted image quality score by fusing the degradation features learned from contrastive learning with the perceptual distortion information extracted by the backbone network. Experimental results on six standard IQA datasets show that SaTQA outperforms the state-of-the-art methods for both synthetic and authentic datasets. Code is available at https://github.com/I2-Multimedia-Lab/SaTQA.

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“…Yang et al [21] presented a simple method that does not use depth map. Two types of objective assessment are taken into account, i.e., the image quality assessment (IQA) and the stereo sense assessment (SSA).…”

Section: Introductionmentioning

confidence: 99%

Stereo Image Quality Assessment Using Visual Attention and Distortion Predictors

Hwang¹

2011

KSII TIIS

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Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

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Objective quality assessment method of stereo images

Cited by 29 publications

References 4 publications

Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning

Stereo Image Quality Assessment Using Visual Attention and Distortion Predictors

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