Reduced-reference quality assessment of DIBR-synthesized images based on multi-scale edge intensity similarity

Zhou, Yu; Liu, Yang; Li, Leida; Gu, Ke; Tang, Lijuan

doi:10.1007/s11042-017-5543-7

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…Low-pass images of a Gaussian type pyramid have been used for edge extraction in the Edge Intensity Similarity metric, EIS [21]. The similarity of edge intensity between appropriate edge images of the reference and the synthesized image pyramid through resolutions has been calculated.…”

Section: Related Workmentioning

confidence: 99%

“…The second group of metrics contains training-free metrics designed to evaluate DIBR-synthesized images with rendering distortions, namely blind metrics: Q [30], MNSS [25], Q [60], OMIQA [47], APT [48], NIQSV [49], NIQSV+ [50] and reference-based metrics Q [59], LoGS [35], PSNR' [64], EIS [21], EM-IQA [61], MP-PSNR [19], CT-IQA [62], MW-PSNR [26], BF-M [63]. The performances of MNSS are taken from [25].…”

Section: E Comparison Of the Proposed Model To Other Iqa Modelsmentioning

confidence: 99%

“…The simple weighted PSNR, PSNR' [64], compensates the object shift and uses a disparity map as a mask to weight the final distortion. The performances of multi-scale edge intensity similarity metric, EIS, are taken from [21]. The training-free metrics of the second group can successfully predict quality of images from IRCCyN/IVC image dataset although there is a need for the performance improvement.…”

Section: E Comparison Of the Proposed Model To Other Iqa Modelsmentioning

confidence: 99%

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Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Sandic-Stankovic¹,

Kukolj

Callet

2022

IEEE Trans. on Image Process.

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Images synthesized using depth-imagebasedrendering (DIBR) techniques may suffer from complex structural distortions. The goal of the primary visual cortex and other parts of brain is to reduce redundancies of input visual signal in order to discover the intrinsic image structure, and thus create sparse image representation. Human visual system (HVS) treats images on several scales and several levels of resolution when perceiving the visual scene. With an attempt to emulate the properties of HVS, we have designed the no-reference model for the quality assessment of DIBR-synthesized views. To extract a higher-order structure of high curvature which corresponds to distortion of shapes to which the HVS is highly sensitive, we define a morphological oriented Difference of Closings (DoC) operator and use it at multiple scales and resolutions. DoC operator nonlinearly removes redundancies and extracts fine grained details, texture of an image local structure and contrast to which HVS is highly sensitive. We introduce a new feature based on sparsity of DoC band. To extract perceptually important loworder structural information (edges), we use the non-oriented Difference of Gaussians (DoG) operator at different scales and resolutions. Measure of sparsity is calculated for DoG bands to get scalar features. To model the relationship between the extracted features and subjective scores, the general regression neural network (GRNN) is used. Quality predictions by the proposed DoC-DoG-GRNN model show higher compatibility with perceptual quality scores in comparison to the tested state-of-theart metrics when evaluated on four benchmark datasets with synthesized views, IRCCyN/IVC image/video dataset, MCL-3D stereoscopic image dataset and IST image dataset.

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Section: Related Workmentioning

confidence: 99%

Section: E Comparison Of the Proposed Model To Other Iqa Modelsmentioning

confidence: 99%

Section: E Comparison Of the Proposed Model To Other Iqa Modelsmentioning

confidence: 99%

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Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Sandic-Stankovic¹,

Kukolj

Callet

2022

IEEE Trans. on Image Process.

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“…Texture features extraction on night-time image composes of two main modules, which are down-sampling, and texture features computation. Down-sampling [43] is used to generate night-time images with different resolutions so as to line with the characteristics of the HVS. Texture character computation works as follows.…”

Section: Texture Feature Extractionmentioning

confidence: 99%

Blind Quality Metric via Measurement of Contrast, Texture, and Colour in Night-Time Scenario

2021

KSII TIIS

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Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.

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“…In order to mimic the hierarchical structure of HVS, multi-resolution and multi-scale image representations have been used in image quality assessment models. Multiresolution and multi-scale image representations have also been used in image quality assessment models designed for images: morphological pyramid of Laplacian type [3][4][5], Gaussian type pyramid [6], multiresolution representation generated using only downsampling [7], [8], morphological wavelets [9] and linear wavelets [10], The sparsity of morphological pyramid and wavelet bands have been used in the image quality model based on general regression neural network [12].…”

Section: Introductionmentioning

confidence: 99%

Morphological Difference of Closings Operator for No-Reference Quality Evaluation of DIBR-Synthesized Images

Sandic-Stankovic¹,

Kukolj

Callet

2022

2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC)

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Images synthesized using Depth-Image-Based Rendering (DIBR) techniques are characterized by complex structural distortion. Multi-resolution multi-scale sparse image representation generated using morphological Difference of Closings operator (DoC) is used to efficiently capture structure-related distortion of synthesized images in the noreference DoC-GRNN image quality assessment model. Nonlinear morphological Difference of Closings operator (DoC) with an array of line-shaped structuring elements of increasing length is used to extract perceptually important details of object structure at different scales and resolutions. The sparsity of DoC band is calculated as scalar feature. The extracted features are mapped to the quality score by general regression neural network (GRNN). We have explored the influence of the direction of an array of line-shaped structuring elements on the model's performances. The DoC-GRNN model shows high agreement with perceptual quality scores, comparable to the state-of-the-art metrics, when evaluated on the stereoscopic DIBR-synthesized images of MCL-3D dataset.

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Reduced-reference quality assessment of DIBR-synthesized images based on multi-scale edge intensity similarity

Cited by 9 publications

References 38 publications

Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Blind Quality Metric via Measurement of Contrast, Texture, and Colour in Night-Time Scenario

Morphological Difference of Closings Operator for No-Reference Quality Evaluation of DIBR-Synthesized Images

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