Subjective similarity evaluation for scenic bilevel images

In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and si...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 156 - 160
Main Authors Yuanhao Zhai, Neuhoff, David L., Pappas, Thrasyvoulos N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
bilevel image similarity
Compression algorithms
Encoding
Image coding
image quality
Measurement
subjective evaluation
Testing
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Summary:In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and silhouettes, scenic bilevel images contain natural scenes, e.g., landscapes and portraits. Seven scenic images were each distorted in forty-four ways, including random bit flipping, dilation, erosion and lossy compression. To produce subjective similarity ratings, the distorted images were each viewed by 77 subjects. These are then used to compare the performance of four compression algorithms and to assess how well percentage error and SmSIM work as bilevel image similarity metrics. These subjective ratings can also provide ground truth for future tests of objective bilevel image similarity metrics.
ISSN:1520-6149
DOI:10.1109/ICASSP.2014.6853577