Open AccessFast large‐scale image enlargement method with a novel evaluation approach: benchmark function‐based peak signal‐to‐noise ratio
Author(s) -
Guo ShuMei,
Hsu ChihYuan,
Kuo GiaHao,
Tsai Jason ShengHong
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0293
Subject(s) - benchmark (surveying) , signal to noise ratio (imaging) , computer science , signal (programming language) , noise (video) , function (biology) , image (mathematics) , scale (ratio) , peak signal to noise ratio , artificial intelligence , algorithm , computer vision , pattern recognition (psychology) , physics , telecommunications , geodesy , geography , quantum mechanics , evolutionary biology , biology , programming language
An objective novel evaluation approach, implemented by the benchmark function‐based peak signal‐to‐noise ratio, particularly suitable for evaluating the performance of a large‐scale enlargement of a small size image is proposed in this study. Also, a fast large‐scale image enlargement method via the improved discrete cosine transform (DCT) is proposed to improve the quality and speed of image zooming. The proposed image enlargement algorithm based on DCT saves computation time by multiplication of the DCT matrix. Compared with the traditional DCT approach, the improved approach overcomes the image shifting and blocky effects. In comparisons with other interpolation methods, DCT enlargement outperforms them in edge details because it considers the global frequency information of the whole image. With the DCT enlargement, it is easy to implement the arbitrary pixel‐size‐based zooming of an image by employing the different size of transform matrix. Illustrative examples show the effectiveness of the proposed approach.