Journal ArticleDOI
No-reference image quality assessment based on hybrid model
TLDR
A computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches is proposed, which demonstrates very competitive quality prediction performance of the proposed method.Abstract:
The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method.read more
Citations
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Journal ArticleDOI
On the use of deep learning for blind image quality assessment
TL;DR: The best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image, having a linear correlation coefficient with human subjective scores of almost 0.91.
Journal ArticleDOI
A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms
Shahrukh Athar,Zhou Wang +1 more
TL;DR: This work carries out the largest performance evaluation study so far on FR fusion methods, and an important discovery is that rank aggregation based FR fusion is able to outperform not only other FR fusion approaches but also the top performing FR methods.
Posted Content
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement.
TL;DR: A very simple but effective metric for predicting quality of contrast-altered images based on the fact that a high-contrast image is often more similar to its contrast enhanced image is proposed.
Journal ArticleDOI
A Novel Hybrid CNN-SVR for CRISPR/Cas9 Guide RNA Activity Prediction.
TL;DR: It is demonstrated that CNN-SVR can effectively exploit features interactions from feed-forward directions to learn deeper features of gRNAs and their corresponding epigenetic features and outperforms available state-of-the-art methods in terms of prediction accuracy, generalization, and robustness.
Journal ArticleDOI
On the Use of Deep Learning for Blind Image Quality Assessment
TL;DR: DeepBIQ as mentioned in this paper uses a Support Vector Regression (SVR) machine to estimate the image quality by average pooling the scores predicted on multiple sub-regions of the original image.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Posted Content
Rich feature hierarchies for accurate object detection and semantic segmentation
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Journal ArticleDOI
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
Journal ArticleDOI
No-Reference Image Quality Assessment in the Spatial Domain
TL;DR: Despite its simplicity, it is able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms.