Full-reference image quality assessment based on image segmentation with edge feature

Shi, Zaifeng; Zhang, Jiaping; Cao, Qingjie; Pang, Ke; Luo, Tiejian

doi:10.1016/j.sigpro.2017.11.015

Cited by 32 publications

(16 citation statements)

References 26 publications

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“…We use different matting algorithms to estimate alpha masks from the original and noisy images, respectively. We evaluated the similarity between the original alpha mask and the noise alpha mask by combining gradient amplitude similarity and gradient direction similarity [42] with the Gaussian–Hermite moment. The reference image and the test image were first divided into non‐overlapping chunks [43].…”

Section: Proposed Methodsmentioning

confidence: 99%

Evaluating the robustness of image matting algorithm

Yuan

Fan

2020

CAAI trans. intell. technol.

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In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises.

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Section: Proposed Methodsmentioning

confidence: 99%

Evaluating the robustness of image matting algorithm

Yuan

Fan

2020

CAAI trans. intell. technol.

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“…If original pristine reference image is available against the stitched image whose quality is to be assessed, then such approach is referred to as Full Reference Image Quality Assessment (FRIQA) [8]. Performance metrics of FRIQA are Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM).…”

Section: A Full Reference Image Quality Assessment (Friqa)mentioning

confidence: 99%

“…The perceptual quality of stitched image is examined with the help of objective image quality assessment approach like FRIQA and RRIQA. When original pristine reference image is available against the stitched image whose quality is to be assessed, then such approach is FRIQA [8] while RRIQA possesses partial information regarding original reference image apart from stitched image [9].…”

Section: Introductionmentioning

confidence: 99%

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Vaidya*,

Gandhe

2020

IJITEE

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Image Stitching is becoming more popular in field of computer vision because of rapid development of efficient algorithms that replaces the high cost wider lens cameras and commercial image stitching tools. The existing methods used global geometric transformation in registration stage and hence suffered from object deformation, parallax error, ghosting effect and motion blur in output result. In this paper, newly developed Hybrid Warping of weighted linearized homography matrix and similarity transform matrix is implemented over standard image stitching database. The visual quality of stitched image using proposed method has been examined in terms of performance metrics of Full Reference Image Quality Assessment (FRIQA) such as Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Reduced Reference Image Quality Assessment (RRIQA). Also, the performance analysis of proposed method is compared against existing image stitching methods in terms of field of view and stitching time. This analysis has ascertained the outperformance of Novel Hybrid Image Stitching method.

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“…In this work, to evaluate an NR metric, six publicly available large IQA datasets were used: TID2013 [41], TID2008 [42], CSIQ [43], LIVE [7], LIVE WIQC [44], and the Waterloo Exploration Database (WE) [55]. The first four datasets are typically selected to evaluate recently introduced measures [34,56,57], while the WE dataset, similar to the Group MAD Competition [58,59], is tied with a novel evaluation methodology. The LIVE WIQC dataset contains subjective scores collected in an uncontrolled manner using the Amazon Mechanical Turk.…”

Section: Datasets and Protocolmentioning

confidence: 99%

No-Reference Image Quality Assessment with Local Gradient Orientations

Oszust

2019

Symmetry

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Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.Symmetry 2019, 11, 95 2 of 20 patches [28] can be found in the literature.Since the supervised learning bridges image statistics with the perceptual quality, it is often applied to obtain a model used for the quality prediction. For the learning, the Support Vector Regression (SVR) [20,21,23,27], neural networks [24], or random forests [29] are applied. In methods that do not use supervised learning, distortion types are modeled with a set of centroids of quality levels or NSS from multiple cues [30,31]. Another direction is to employ a pseudo-reference image which is created and compared with a distorted image with blockiness, sharpness, and noisiness metrics [32]. Recently, many NR-IQA approaches which use deep neural network (DNN) architectures have been introduced. They merge the feature extraction and quality prediction steps. However, they suffer from a small number of training examples available in IQA benchmark datasets or use complex architectures that are devoted to image recognition tasks. To overcome these limitations, most of them use image patches [33,34], train models using FR-IQA measures instead of subjective scores [34,35], or perform fine-tuning to adopt an architecture to the IQA [36]. Interestingly, some DNN-based approaches use features introduced in earlier methods [35].The HVS is sensitive to local structures, which are often described using local binary patterns (LBP) and gradient-based statistics. However, a spatial distribution of LBP may not be able to capture more complex structures [37]. Thus, statistics extracted from gradient maps often occur in conjunction with other approaches to improve the IQA performance [27,38]. Such techniques use global distributions of gradient magnitude maps [25], relative gradient orientations or magnitude [24,39].To describe an image and efficiently take into account local gradient orientations, Histogram of Oriented Gradients (HOG) descriptor can be used [40]. However, the HOG produces high-dimensional feature vectors, which are devoted to object recognition...

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Full-reference image quality assessment based on image segmentation with edge feature

Cited by 32 publications

References 26 publications

Evaluating the robustness of image matting algorithm

Evaluating the robustness of image matting algorithm

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

No-Reference Image Quality Assessment with Local Gradient Orientations

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