Full-reference quality assessment of stereoscopic images using disparity-gradient-phase similarity

Li, Feifei; Shen, Liquan; Wu, Dapeng; Fang, Ruigang

doi:10.1109/chinasip.2015.7230486

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…An application where the spatial alignment of images cannot be taken for granted is a quality assessment for stereoscopic 3-D. Changes in disparity will cause the left and right image to shift, often in spatially nonuniform way. Li et al 48 showed how the disparity information can be used to undo the stereoscopic projection and merge left and right eye into a cyclopean image where luminance features can be compared in a similar way as in SSIM.…”

Section: Discussionmentioning

confidence: 99%

A transformation-aware perceptual image metric

2015

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Abstract. Predicting human visual perception has several applications such as compression, rendering, editing, and retargeting. Current approaches, however, ignore the fact that the human visual system compensates for geometric transformations, e.g., we see that an image and a rotated copy are identical. Instead, they will report a large, false-positive difference. At the same time, if the transformations become too strong or too spatially incoherent, comparing two images gets increasingly difficult. Between these two extrema, we propose a system to quantify the effect of transformations, not only on the perception of image differences but also on saliency and motion parallax. To this end, we first fit local homographies to a given optical flow field, and then convert this field into a field of elementary transformations, such as translation, rotation, scaling, and perspective. We conduct a perceptual experiment quantifying the increase of difficulty when compensating for elementary transformations. Transformation entropy is proposed as a measure of complexity in a flow field. This representation is then used for applications, such as comparison of nonaligned images, where transformations cause threshold elevation, detection of salient transformations, and a model of perceived motion parallax. Applications of our approach are a perceptual level-of-detail for real-time rendering and viewpoint selection based on perceived motion parallax. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Discussionmentioning

confidence: 99%

A transformation-aware perceptual image metric

2015

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“…The algorithm provides great results when tested on the LIVE 3D image Phase I database, the MCL-3D image database 12 and the IRCCYN/IVC 3D image database. 13 Li et al 14 developed an FR IQA metric for S3D images based on the disparity-gradient-phase-congruency similarity (DGP-SIM). Measurements of the three feature similarity indexes of the extracted disparity maps of the distortion-free reference and distorted stereoscopic pairs, gradient magnitude maps and phase congruency maps of distortion-free reference and distorted cyclopean images, are combined together to obtain a single stereoscopic image quality index score.…”

Section: Fr Objective Metricsmentioning

confidence: 99%

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Biswas

Appina

Tamboli³

et al. 2021

Applications of Machine Learning 2021

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Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.

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“…where k is a constant to avoid zero-by-zero condition. The non structural similarity can be obtained from (2).…”

Section: Let a Imentioning

confidence: 99%

“…They consider the absolute difference between left and right images as disparity map and its pixel value is considered as weights in evaluating weighted difference between reference and test stereo pairs. Feifei et al [2] proposed a cyclopean based approach where they combined three features from binocular disparity, gradient magnitude, and phase congruency into one quality score. They include both the binocular combination characteristic and disparity effect in their proposed stereoscopic image quality assessment (SIQA).…”

Section: Introductionmentioning

confidence: 99%

Sparsity based stereoscopic image quality assessment

Khan

Channappayya

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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In this work, we present a full-reference stereo image quality assessment algorithm that is based on the sparse representations of luminance images and depth maps. The primary challenge lies in dealing with the sparsity of disparity maps in conjunction with the sparsity of luminance images. Although analysing the sparsity of images is sufficient to bring out the quality of luminance images, the effectiveness of sparsity in quantifying depth quality is yet to be fully understood. We present a full reference Sparsity-based Quality Assessment of Stereo Images (SQASI) that is aimed at this understanding.

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Full-reference quality assessment of stereoscopic images using disparity-gradient-phase similarity

Cited by 11 publications

References 15 publications

A transformation-aware perceptual image metric

A transformation-aware perceptual image metric

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Sparsity based stereoscopic image quality assessment

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