3D saliency guided deep quality predictor for no-reference stereoscopic images

Messai, Oussama; Chetouani, Aladine; Hachouf, Fella; Seghir, Zianou Ahmed

doi:10.1016/j.neucom.2022.01.002

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…It is worth noting that the proposed metric scheme takes the input RGB (Red, Green, Blue) stereo image without any pre-processing and provides four output scores (e.i., left, right, stereo and global score), whereas most SIQA metrics convert the input images to a typical gray tone as a pre-treatment step for the CNN model and give a single score. As demonstrated in our recent work [17], using three channels as input rather than a single gray scale tends to increase the performance by preserving the perceived original distortion effects and judged by the human during the evaluation process.…”

Section: Proposed Multi-score Methodsmentioning

confidence: 81%

“…The diminution is similar for both datasets. Metrics Waterloo-P1/Waterloo-P2 Waterloo-P2/Waterloo-P1 Liu [12] 0.696 0.701 Yang [14] 0.781 0.864 Chen [22] 0.806 0.846 Saliency-SIQA [17] 0.826 0.848 DECOSINE [13] 0.842 0.873 Wang [15] 0.856 0.881 Proposed 0.944 0.940 0.9. Compared to the second best metric (i.e., Wang), the improvement accuracy of quality assessment was found to be 10% in terms of PLCC.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

“…During the training, we do not consider data augmentation because the noted subjective score may differ if data augmentation techniques are used (e.g., rotation, flipping, etc.) [17].…”

Section: Dataset and Training Protocolmentioning

confidence: 99%

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End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

Messai

Chetouani

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitating the learning of unique quality indicators for each view. Unlike existing stereoscopic IQA measures which focus mainly on estimating a global human score, we suggest incorporating left, right, and stereoscopic objective scores to extract the corresponding properties of each view, and so forth estimating stereoscopic image quality without reference. Therefore, we use a deep multi-score Convolutional Neural Network (CNN). Our model has been trained to perform four tasks: First, predict the left view's quality. Second, predict the quality of the left view. Third and fourth, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality. Experiments are conducted on Waterloo IVC 3D Phase 1 and Phase 2 databases. The results obtained show the superiority of our method when comparing with those of the state-of-the-art. The implementation code can be found at: https://github.com/o-messai/multiscore-SIQA

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Section: Proposed Multi-score Methodsmentioning

confidence: 81%

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

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End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

Messai

Chetouani

2022

2022 IEEE International Conference on Image Processing (ICIP)

Self Cite

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“…Jiang et al [42] Proposed a unified quality evaluation model for singly and multiply distorted stereoscopic images by learning visual primitives based on a supervised dictionary framework to encode quality related features. Messai et al [43], [44], [45] created cyclopean images in the first stage, followed by predicting scores based on machine learning or convolutional neural network (CNN). Oh et al [46] built a deep CNN for blind SIQA trained through two-step regression, where the first step is responsible for automatically extracting local features, and the second part aggregates the local features into global features.…”

Section: Realated Workmentioning

confidence: 99%

Blind Quality Assessment of Stereoscopic Images Considering Binocular Perception Based on Shearlet Decomposition

Wan,

Jiang,

Shen

2023

IEEE Access

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Due to the deficient knowledge of binocular vision properties, how to effectively evaluate stereoscopic images still remains a challenging task. Inspired by multichannel processing of human visual system (HVS), we propose a blind method for stereoscopic image quality assessment (SIQA) by extracting quality related features in sub-bands of the image. First of all, we introduce the shearlet transform to decompose the left-and right-view images into multiple sub-bands content with diverse combinations of scales and orientations, and obtain the combined view based on energy-weighted summation of the corresponding sub-bands of two eye views. Then, natural scene statistics (NSS) of the original left and right images are obtained as quality-sensitive features, followed by extracting NSS features of the sub-bands of left, right and combined views. Moreover, we calculate the gradient similarity between each sub-band pair to denote the asymmetric distortion and disparity information. Finally, all the extracted features are mapped into a quality score by support vector regression (SVR). experimental results on multiple benchmark databases verify the superiority of our method.

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Octonion-based transform moments for innovative stereo image classification with deep learning

Tahiri,

Boudaaoua,

Karmouni

et al. 2024

Complex Intell. Syst.

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Recent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images.

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3D saliency guided deep quality predictor for no-reference stereoscopic images

Cited by 9 publications

References 45 publications

End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

Blind Quality Assessment of Stereoscopic Images Considering Binocular Perception Based on Shearlet Decomposition

Octonion-based transform moments for innovative stereo image classification with deep learning

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