No-reference image quality assessment with shearlet transform and deep neural networks

Li, Yuming; Po, Lai-Man; Xu, Xuyuan; Feng, Litong; Fang, Yuan; Cheung, Chun-Ho; Cheung, Kmc

doi:10.1016/j.neucom.2014.12.015

Cited by 85 publications

(41 citation statements)

References 30 publications

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“…Similarly, Saad et al [18] transform each image using discrete consine transform and the resulting coefficients are used for a generalized gaussian density model. Later, Li et al [14] proposed a NR-IQA method using neural network to extract features in the domain of shearlet. But the auto-encoder used in their work is different from CNNs.…”

Section: Related Workmentioning

confidence: 99%

“…For NR-IQA, methods can be grouped into two main categories, Natural Scence Statistics (NSS)-based and training-based. The former one aims at seeking "naturalness" among undistorted images so that "unnatural" distortion signal can be easily detected [6,[14][15][16]18]. For the training-based method, it relies on a set of features learned from images and then a classifier is trained.…”

Section: Introductionmentioning

confidence: 99%

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

Jia

Zhang

2017

Multimed Tools Appl

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In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map. We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al. (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset. To mimic human vision, we introduce saliency maps combining with CNN to propose a Saliency-based DCNN (SDCNN) framework for NR-IQA. We compute a saliency map for each image and both the map and the image are split into small patches. Each image patch is assigned with a patch importance value based on its saliency patch. A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image. Our experimental results show that the SDCNN framework is superior to other state-of-the-art approaches on the widely used LIVE dataset. The TID2008 and the CISQ image quality datasets are utilised to report cross-dataset results. The results indicate that our proposed SDCNN can generalise well on other datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jia

Zhang

2017

Multimed Tools Appl

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“…al [12] proposed a machine-learning-based method that uses the histograms of local ternary pattern (LTP) as features for the training procedure. Li et al [13] proposed a deep-neural-network-based algorithm that extracts features using shearlet transform and evolves the features using stacked auto-encoders. Then the differences of evolved features are identified by a softmax classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

A No-Reference Adaptive Blockiness Measure for JPEG Compressed Images

Tang

Wang

2016

PLoS ONE

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Digital images have been extensively used in education, research, and entertainment. Many of these images, taken by consumer cameras, are compressed by the JPEG algorithm for effective storage and transmission. Blocking artifact is a well-known problem caused by this algorithm. Effective measurement of blocking artifacts plays an important role in the design, optimization, and evaluation of image compression algorithms. In this paper, we propose a no-reference objective blockiness measure, which is adaptive to high frequency component in an image. Difference of entropies across blocks and variation of block boundary pixel values in edge images are adopted to calculate the blockiness level in areas with low and high frequency component, respectively. Extensive experimental results prove that the proposed measure is effective and stable across a wide variety of images. It is robust to image noise and can be used for real-world image quality monitoring and control. Index Terms—JPEG, no-reference, blockiness measure

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“…15 These methods often outperform traditional approaches that use hand-crafted features. [16][17][18][19] Cruz-Roa et al 15 proposed a three-layer convolutional neural network (CNN) method for invasive ductal carcinoma detection in histopathology images of breast cancer and compared their method with hand-crafted features. They reported 6% improvement in the classification accuracy when using their CNN.…”

Section: Introductionmentioning

confidence: 99%

“…They won the International Conference on Pattern Recognition 2012 competition. Li et al 19 used shearlet transform and deep neural networks for image quality assessment. They extracted features using the sum of subband shearlet coefficients and used stacked autoencoders as their main neural network building blocks.…”

Section: Introductionmentioning

confidence: 99%

Microscopic medical image classification framework via deep learning and shearlet transform

2016

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Abstract. Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computeraided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

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No-reference image quality assessment with shearlet transform and deep neural networks

Cited by 85 publications

References 30 publications

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

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

A No-Reference Adaptive Blockiness Measure for JPEG Compressed Images

Microscopic medical image classification framework via deep learning and shearlet transform

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