Representative image thumbnails: automatic and manual

Samadani, Ramin; Mauer, Timothy; Berfanger, David; Clark, James H.; Bausk, Brett

doi:10.1117/12.765264

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…This is primarily because of the non-adaptive nature of the filtering kernel, which is independent of the image content. The same phenomenon is observed in more recent image interpolation techniques [9], [10], [11], [12]. Motivated by the working principle of bilateral filtering [13], the authors in [14] proposed to adapt the kernel shapes to local image patches.…”

Section: Introductionsupporting

confidence: 61%

Nonlocal Co-occurrence for Image Downscaling

Ghosh¹

2020

Preprint

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Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernelbased convolutional filters could be modified to develop efficient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image filtering concept. We propose to use pairwise co-occurrence similarity of the pixelpairs as the range kernel similarity in the filtering operation. The cooccurrence of the pixel-pair is learned directly from the input image. This co-occurrence learning is performed in a neighborhood based fashion all over the image. The proposed method can preserve the high-frequency structures, which were present in the input image, into the downscaled image. The resulting images retain visuallyimportant details and do not suffer from edge-blurring artifact. We demonstrate the effectiveness of our proposed approach with extensive experiments on a large number of images downscaled with various downscaling factors.

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