Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems

Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli

Research output: Contribution to journalArticlepeer-review


The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathered for this purpose have provided useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we use eleven full-reference IQA models to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages in these tasks, and propose a set of desirable properties for incorporation into future IQA models.

Original languageEnglish (US)
Pages (from-to)1258-1281
Number of pages24
JournalInternational Journal of Computer Vision
Issue number4
StatePublished - Apr 2021


  • Image quality assessment
  • Perceptual optimization
  • Performance evaluation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems'. Together they form a unique fingerprint.

Cite this