DeepSim: Deep similarity for image quality assessment

Gao, Fei; Wang, Yi; Li, Panpeng; Tan, Min; Yu, Jun; Zhu, Yanzi

doi:10.1016/j.neucom.2017.01.054

Cited by 154 publications

(69 citation statements)

References 32 publications

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“…4.2) and two existing datasets (CSIQ [26] and TID2013 [42]). Since there are many recent learning-based algorithms (e.g., [17,24,28]), in this paper we only compare against the three that performed the best on established datasets based on their published results [7,25]. We show comparisons against other methods and on other datasets in the supplementary.…”

Section: Resultsmentioning

confidence: 99%

“…To address these limitations, some have proposed IQA methods based on machine learning to learn more sophisticated models [19]. Although many learning-based methods use hand-crafted image features [8,10,18,20,21,31,33,35,40], recent methods (including ours) apply deep-learning to FR-IQA to learn features automatically [7,17,25]. However, the accuracy of all existing learning-based methods depends on the size and quality of the datasets they are trained on, and existing IQA datasets are small and noisy.…”

Section: Introductionmentioning

confidence: 99%

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PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference

Prashnani

Cai

Mostofi

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

258

192

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The ability to estimate the perceptual error between images is an important problem in computer vision with many applications. Although it has been studied extensively, however, no method currently exists that can robustly predict visual differences like humans. Some previous approaches used hand-coded models, but they fail to model the complexity of the human visual system. Others used machine learning to train models on human-labeled datasets, but creating large, high-quality datasets is difficult because people are unable to assign consistent error labels to distorted images. In this paper, we present a new learning-based method that is the first to predict perceptual image error like human observers. Since it is much easier for people to compare two given images and identify the one more similar to a reference than to assign quality scores to each, we propose a new, large-scale dataset labeled with the probability that humans will prefer one image over another. We then train a deep-learning model using a novel, pairwise-learning framework to predict the preference of one distorted image over the other. Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels. The perceptual error estimated by our new metric, PieAPP, is well-correlated with human opinion. Furthermore, it significantly outperforms existing algorithms, beating the state-of-the-art by almost 3× on our test set in terms of binary error rate, while also generalizing to new kinds of distortions, unlike previous learning-based methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference

Prashnani

Cai

Mostofi

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

258

192

View full text Add to dashboard Cite

show abstract

“…Learning based Metric. As well as the heuristic-based metric, there are numerous learning based metrics [7,19,48], for comparing images in a perceptual-based manner which have been used to evaluate image compression and many other imaging tasks. We refer readers to a recent survey [80] for a comprehensive review of various deep features adopted for perceptual metrics.…”

Section: Related Workmentioning

confidence: 99%

Scoot: A Perceptual Metric for Facial Sketches

Fan

Zhang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Human visual system has the strong ability to quick assess the perceptual similarity between two facial sketches. However, existing two widely-used facial sketch metrics, e.g., FSIM and SSIM fail to address this perceptual similarity in this field. Recent study in facial modeling area has verified that the inclusion of both structure and texture has a significant positive benefit for face sketch synthesis (FSS). But which statistics are more important, and are helpful for their success? In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics. To test the quality of metrics, we propose three novel meta-measures based on various reliable properties. Extensive experiments demonstrate that our Scoot metric exceeds the performance of prior work. Besides, we built the first large scale (152k judgments) human-perception-based sketch database that can evaluate how well a metric is consistent with human perception. Our results suggest that "spatial structure" and "co-occurrence texture" are two generally applicable perceptual features in face sketch synthesis.

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“…Other IQMs based on modelling the low-level vision have also been proposed, such the spatial CIELAB (S-CIELAB) [5]. In recent years the use of deep learning has attracted attention of many researchers [6,7,8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

On the Use of a Convolutional Neural Network to Predict Perceptual Quality of Images without Reference for Different Viewing Distances

Chetouani¹,

Pedersen²

2019

2019 IEEE International Conference on Image Processing (ICIP)

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A plethora of image quality metrics have been proposed in the literature. These metrics aims to estimate the perceptual image quality automatically. One important key aspect that the perceived quality is dependent on is the viewing distance from the observer to the image. In this study, we propose to consider this information by estimating the quality of a given image without a reference image for different viewing distances. For that, a Convolutional Neural Network (CNN) model was used in this study. Relevant patches are first selected from the image and they are then used as inputs to the CNN. The selection is here based on saliency information. The used CNN is composed of two outputs that correspond to the predicted subjective scores for two viewing distances (50 cm and 100 cm). Our method was evaluated using the Colourlab Image Database: Image Quality (CID:IQ) that provides subjective scores at two different viewing distances. The obtained results show the efficiency of our method.

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DeepSim: Deep similarity for image quality assessment

Cited by 154 publications

References 32 publications

PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference

PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference

Scoot: A Perceptual Metric for Facial Sketches

On the Use of a Convolutional Neural Network to Predict Perceptual Quality of Images without Reference for Different Viewing Distances

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