Saliency-based deep convolutional neural network for no-reference image quality assessment

Jia, Sen; Zhang, Yang

doi:10.1007/s11042-017-5070-6

Cited by 65 publications

(19 citation statements)

References 24 publications

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“…Nevertheless, the problem is that they ignore that the visual quality of different local regions is often different and humans tend to concentrate on the regions of saliency when evaluating an image. Therefore, the salient patches of images can be considered to predict image quality in the following methods [49]- [51].…”

Section: A: Ss As Image Patch Label Methodsmentioning

confidence: 99%

“…The final image quality score is yielded with the weighted average of each image patch. To further improve prediction performance, in [50], [51], they consider only the salient patches to evaluate image quality score. First, they also split the image into patches and use typical saliency detection methods to obtain image saliency map.…”

Section: A: Ss As Image Patch Label Methodsmentioning

confidence: 99%

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A Survey of DNN Methods for Blind Image Quality Assessment

Yang

Liu

2019

IEEE Access

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Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future. INDEX TERMS Deep learning, blind image quality assessment (BIQA), deep neural networks (DNN) model, deep features, quality prediction.

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Section: A: Ss As Image Patch Label Methodsmentioning

confidence: 99%

Section: A: Ss As Image Patch Label Methodsmentioning

confidence: 99%

A Survey of DNN Methods for Blind Image Quality Assessment

Yang

Liu

2019

IEEE Access

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“…In the last few years, Deep Neural Networks (DNNs) are emerging as a highly effective neural network architecture for object recognition, classification tasks [49], speech recognition [50] and other applications for image quality assessment [51]. All of these tasks demonstrate that the performance of DNNs can overcome conventional methods significantly.…”

Section: B Machine Learning Based Approachesmentioning

confidence: 99%

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

et al. 2020

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Estimation of Remaining Useful Life (RUL) is a crucial task in Prognostics and Health Management (PHM) for condition-based maintenance of machinery. In order to transmit and store the sensor data for archiving and long term analysis, data compression techniques are regularly used to reduce the requirements of bandwidth, energy and storage in modern remote PHM systems. In these systems the challenge arises of how the compressed sensor data affects the RUL estimation algorithms. A main drawback of conventional statistical modeling approaches is that they require expert prior knowledge and a significant number of assumptions. Alternative regression based approaches and deep neural networks are known to have issues when modeling long-term dependencies in the sequential data. Recently Long Short-Term Memory (LSTM) neural networks have been proposed to overcome these issues and in this paper we create a LSTM network and data fusion approach that can estimate the RUL with compressed (distorted) data. The experimental results indicate that the proposed method is able to estimate RUL reliably with narrower error bands compared to other state-of-the-art approaches. Moreover, the proposed method is able to predict RUL from both the raw and compressed datasets with comparable accuracy. INDEX TERMS Machine health monitoring, remaining useful life (RUL), long-short term memory, recurrent neural network, data compression.

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“…In looking at an image, humans tend to fixate on Regions Of Interest (ROIs) and less salient parts of the image are ignored. This attention mechanism of the Human Visual System (HVS) plays an important role in vision tasks such as object classification [11], video analysis [43,45], image compression [44], action classification [34] and quality assessment [19,20]. Simulating where humans gaze in pictures in computer vision is referred to as visual saliency prediction, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

EML-NET: An Expandable Multi-Layer NETwork for saliency prediction

Jia

Bruce

2020

Image and Vision Computing

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117

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Saliency prediction can benefit from training that involves scene understanding that may be tangential to the central task; this may include understanding places, spatial layout, objects or involve different datasets and their bias. One can combine models, but to do this in a sophisticated manner can be complex, and also result in unwieldy networks or produce competing objectives that are hard to balance. In this paper, we propose a scalable system to leverage multiple powerful deep CNN models to better extract visual features for saliency prediction. Our design differs from previous studies in that the whole system is trained in an almost end-to-end piece-wise fashion. The encoder and decoder components are separately trained to deal with complexity tied to the computational paradigm and required space. Furthermore, the encoder can contain more than one CNN model to extract features, and models can have different architectures or be pre-trained on different datasets. This parallel design yields a better computational paradigm overcoming limits to the variety of information or inference that can be combined at the encoder stage towards deeper networks and a more powerful encoding. Our network can be easily expanded almost without any additional cost, and other pre-trained CNN models can be incorporated availing a wider range of visual knowledge. We denote our expandable multi-layer network as EML-NET and our method achieves the state-of-the-art results on the public saliency benchmarks, SALICON, MIT300 and CAT2000.

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Saliency-based deep convolutional neural network for no-reference image quality assessment

Cited by 65 publications

References 24 publications

A Survey of DNN Methods for Blind Image Quality Assessment

A Survey of DNN Methods for Blind Image Quality Assessment

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

EML-NET: An Expandable Multi-Layer NETwork for saliency prediction

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