68‐1: A Machine Learning Approach to Objective Image Quality Evaluation | Zendy

Ribera Javier | Zendy; Cook Gregory W. | Zendy; Stolitzka Dale | Zendy; Xiong Wei | Zendy

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68‐1: A Machine Learning Approach to Objective Image Quality Evaluation

Author(s) -

Ribera Javier,

Cook Gregory W.,

Stolitzka Dale,

Xiong Wei

Publication year - 2019

Publication title -

sid symposium digest of technical papers

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.351

H-Index - 44

eISSN - 2168-0159

pISSN - 0097-966X

DOI - 10.1002/sdtp.13084

Subject(s) - lossless compression , computer science , machine learning , artificial intelligence , image quality , image (mathematics) , quality (philosophy) , image compression , gold standard (test) , standard test image , test (biology) , correlation , computer vision , pattern recognition (psychology) , image processing , data compression , mathematics , statistics , paleontology , philosophy , geometry , epistemology , biology

Subjective Test Scoring is the gold standard for evaluating visual lossless image compression, but its high cost and long lead time prohibits testing more than a few images. Here, we model the display and human eye and use Machine Learning models of increased complexity, achieving a Pearson correlation of 0.95.

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