Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks

Kang, Le; Ye, Peng; Li, Yang; Doermann, David

doi:10.1109/icip.2015.7351311

Cited by 148 publications

(129 citation statements)

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“…Following the same experiment setting in [10,11], the proposed DCNN achieves higher LCC and SROCC scores, 0.9782 and 0.9735 respectively. Our experiment shows saliency maps can further improve CNNs for NR-IQA.…”

Section: Introductionmentioning

confidence: 99%

“…In their work, it has been shown that mean subtracted contrast normalized coefficients can represent statistical properties of distortion after applying the local normalisation. Recent CNN-based NR-IQA methods [10,11], including ours, are also based on the same spatial domain. But the difference is that we try to use CNNs to learn quality features instead of seeking the naturalness.…”

Section: Related Workmentioning

confidence: 99%

“…It has been shown that CNN-learned features outperform hand-designed ones (local binary patterns or scale-invariant feature transform) in many areas, such as object classification [7], face gender recognition [8] or fashion detection [9]. More recently, CNNs have been introduced to NR-IQA and achieved state-of-the-art results [10,11,13]. It has also been shown that the depth of CNNs plays an important role in feature extraction [7,8,22,23].…”

Section: Introductionmentioning

confidence: 99%

“…For the training-based method, it relies on a set of features learned from images and then a classifier is trained. The training-based method can be considered as a traditional machine learning task [10,11,13,25,27]. Therefore how to extract discriminant features is a common question between vision recognition tasks and training-based IQA.…”

Section: Introductionmentioning

confidence: 99%

“…However, most existing CNN methods have not introduced HVS into IQA designs. In the works of [10,11], a whole image is split into a set of small patches and the overall estimation is based on the average of those patches. Two similar image patches may be labelled as two totally different noise levels.…”

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: Introductionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%