RankIQA: Learning from Rankings for No-Reference Image Quality Assessment

@article{Liu2017RankIQALF,
  title={RankIQA: Learning from Rankings for No-Reference Image Quality Assessment},
  author={Xialei Liu and Joost van de Weijer and Andrew D. Bagdanov},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1040-1049}
}
We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA. [] Key Method These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images.

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References

SHOWING 1-10 OF 53 REFERENCES
Learning to Rank for Blind Image Quality Assessment
TLDR
This paper explores and exploits preference image pairs such as the quality of image Ia is better than that of image Ib for training a robust BIQA model and investigates the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution.
Group MAD Competition? A New Methodology to Compare Objective Image Quality Models
  • Kede Ma, Q. Wu, Lei Zhang
  • Computer Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
A new mechanism, namely group MAximum Differentiation (gMAD) competition, which automatically selects subsets of image pairs from the database that provide the strongest test to let the IQA models compete with each other, is proposed.
On the use of deep learning for blind image quality assessment
TLDR
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.
Convolutional Neural Networks for No-Reference Image Quality Assessment
TLDR
A Convolutional Neural Network is described to accurately predict image quality without a reference image to achieve state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments.
Image Quality Assessment Using Similar Scene as Reference
TLDR
It is shown that non-aligned image with similar scene could be well used for reference, using a proposed Dual-path deep Convolutional Neural Network (DCNN), and analysis indicates that the model captures the scene structural information and non-structural information “naturalness” between the pair for quality assessment.
A deep neural network for image quality assessment
This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates
Unsupervised feature learning framework for no-reference image quality assessment
TLDR
This paper uses raw image patches extracted from a set of unlabeled images to learn a dictionary in an unsupervised manner and uses soft-assignment coding with max pooling to obtain effective image representations for quality estimation.
A Learning-to-Rank Approach for Image Color Enhancement
TLDR
This work forms the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values.
Learning to compare image patches via convolutional neural networks
TLDR
This paper shows how to learn directly from image data a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems, and opts for a CNN-based model that is trained to account for a wide variety of changes in image appearance.
FSIM: A Feature Similarity Index for Image Quality Assessment
TLDR
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.
...
...