Full-Reference Image Quality Assessment by Combining Features in Spatial and Frequency Domains

Tang, Zhisen; Yao, Zheng; Gu, Ke; Liao, Kaiyang; Wang, Wei; Yu, Miao

doi:10.1109/tbc.2018.2871376

Cited by 35 publications

(17 citation statements)

References 46 publications

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“…Image quality evaluation based on HVS is one of the most reasonable methods when assessing pictures, because the results obtained are consistent with the subjective results [15,28]. Therefore, it is of great significance to study how the human visual system works.…”

Section: Human Visual System (Hvs)mentioning

confidence: 75%

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

et al. 2020

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Stitching gaps and misalignments in mosaic images can severely degrade the human visual perception of mosaic effects. Image stitching plays a key role in eliminating these unpleasant defects. In this paper, an image-stitching method for mosaic images with invisible seams is proposed, according to the research on the human visual system (HVS). By quantifying the human visual attention of images and visual discrimination about luminance difference and fine dislocations, each pixel in the stitching region is given a priority value for tracing a stitching line. Coupled with the processing of an optimal stitching line locating method and the multi-band blending algorithm, the pixels of discontinuous items in mosaic images decrease significantly and the stitching line is almost invisible. This study provides a new insight into the image-stitching field, and the experiments show that the results of the proposed method are more consistent with the human visual system in creating high-quality image mosaics.

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Section: Human Visual System (Hvs)mentioning

confidence: 75%

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

et al. 2020

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“…Compared with other learning-based models, RF has only two parameters, and they are set by default. Meanwhile, RF exhibits the outstanding ability in terms of prediction accuracy [8]. Specifically, we show that RF outperforms SVR and NN both in prediction accuracy and robustness.…”

Section: Related Workmentioning

confidence: 80%

“…The learning-based methods are extensively applied in the field of IQA, including neural networks (NN) [22,23], support vector regression (SVR) [24][25][26], extreme learning machine (ELM ) [27] and random forest (RF) [8,28,29]. In [24]，multiple features are employed for complementary representation of image quality and SVR is used as a regression tool.…”

Section: Related Workmentioning

confidence: 99%

“…Because of the great success of SSIM, its extended versions, called MS-SSIM [6] and IW-SSIM [7], have been introduced by multiscale and information content weighted SSIM. Moreover, the gradient magnitude (GM) which can effectively capture the local structural information is widely applied in IQA studies [8][9][10]. FSIM [9] introduces the gradient magnitude (GM) and phase congruency (PC) as the complementary features to capture the image quality.…”

Section: Introductionmentioning

confidence: 99%

“…Other IQA models avoid the drawback by using a weighted summation, but there is no universal method available for appropriate the weighting coefficients, which need to be determined in weighted summation. Moreover, [8] and [27] have researched the simple summation or multiplication may lead to the relationship between the distortions and image quality score to be linear in the methods of above-mentioned. Fortunately, the pooling strategies by using machine learning can overcome the drawbacks, which introduce the human vision knowledge to the trained model.…”

Section: Introductionmentioning

confidence: 99%

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Image Quality Assessment by Combining Masking Texture and Perceptual Color Difference Model

Tang¹,

Zheng²,

Wang³

et al. 2020

KSII TIIS

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Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

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Weighted ensemble networks formultiviewbased tiny object quality assessment

Zhou

Wu²,

Zou

et al. 2021

Concurrency and Computation

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As demand for intelligent manufacturing continues to grow, tiny object quality assessment (TOQA) is becoming increasingly importance in industrial automation. Recently, visual-based TOQA has attracted an increasing attention, since the physical appearance is the foremost assessment index for evaluating the tiny object quality. It is exhausted and challenging to determine the quality of tiny object by manual visual inspection, and thus some machine vision systems are developed for automatic TOQA. Existing systems often use a limited number of cameras to capture the image of fallen tiny object, and thus may be not reliable since the tiny object may be unsound (such as cracked or damaged) in an invisible side. In this article, we develop a novel system for automatic TOQA that captures images of tiny object from multiple (more than two) view points, and propose a novel method termed weighted ensemble network (WENet) to effectively integrate the information of different views. In particular, convolutional neural networks (CNNs) are adopted to extract features from the images of different views. Then the multiview features are weighted combined for tiny object quality prediction. Traditional ensemble approaches usually directly applying average or voting to the prediction results of different views, or learn fixed weights to combine the results. Different from these approaches, the weights are adaptively determined in our method according to the quality of the captured image, since the features extracted from a low-quality (e.g., blurred) image should contribute less to the final prediction. Handcrafted features and deep features are integrated in a sophisticated way in our method, and we empirically demonstrate the effectiveness of our method on grain quality assessment by investigating different CNN architectures for feature extraction and comparing with the conventional ensemble approaches. K E Y W O R D S ensemble learning, image quality assessment, neural networks, tiny object quality assessment 1 INTRODUCTION With computer vision (CV)-based technologies have attracted intensive attention in tiny object quality assessment (TOQA), other industries are gradually using CV for quality assessment, 1 especially in agriculture. To evaluate tiny tumors in the medical field, a method for segmentation tiny objects is proposed. 2 In industrial defect detection, an efficient online detection system for tiny part defects has also been proposed. 3

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Full-Reference Image Quality Assessment by Combining Features in Spatial and Frequency Domains

Cited by 35 publications

References 46 publications

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

Image Quality Assessment by Combining Masking Texture and Perceptual Color Difference Model

Weighted ensemble networks formultiviewbased tiny object quality assessment

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