Fast large‐scale image enlargement method with a novel evaluation approach: benchmark function‐based peak signal‐to‐noise ratio

Abstract

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.

Image Downscaling Based on Neugebauer Model

“…Image downscaling is a kind of image resizing or scaling techniques, which include image enlargement (1) , image interpolation (2) , image super resolution (3), (4) , contentaware image resizing (5) , making low resolution pixel art from pictures of standard sizes or vector images (6) , and enlargement of pixel art (7) . Downscaling is necessary to view a high-resolution image on digital cameras, mobile devices and social networks.…”