1 research outputs found
Comparison-based Image Quality Assessment for Parameter Selection
Image quality assessment (IQA) is traditionally classified into
full-reference (FR) IQA and no-reference (NR) IQA according to whether the
original image is required. Although NR-IQA is widely used in practical
applications, room for improvement still remains because of the lack of the
reference image. Inspired by the fact that in many applications, such as
parameter selection, a series of distorted images are available, the authors
propose a novel comparison-based image quality assessment (C-IQA) method. The
new comparison-based framework parallels FR-IQA by requiring two input images,
and resembles NR-IQA by not using the original image. As a result, the new
comparison-based approach has more application scenarios than FR-IQA does, and
takes greater advantage of the accessible information than the traditional
single-input NR-IQA does. Further, C-IQA is compared with other
state-of-the-art NR-IQA methods on two widely used IQA databases. Experimental
results show that C-IQA outperforms the other NR-IQA methods for parameter
selection, and the parameter trimming framework combined with C-IQA saves the
computation of iterative image reconstruction up to 80%.Comment: 12 pages, 15 figure