With a flood of display devices with different resolutions, there are increasing demands for efficient image scaling algorithms, which can improve performances of real-time applications such as remote desktop and screen sharing. Image downscaling algorithms can be divided into two classes ― content-adaptive and non-adaptive. Non-adaptive algorithms produce aliasing, blur and halo artifacts though they are very fast. Content-adaptive algorithms have been proposed to improve perceptual quality of scaled images at the expense of computational power.
Many of the content-adaptive downscalers employ non-adaptive algorithms like box filtering and Bicubic as fundamental tools. This means that the performance of those adaptive scaling methods can be improved by using a superior non-adaptive algorithm.
Kim Su Hyon, a researcher at the Faculty of Information Science and Technology, has proposed a new non-adaptive spatial filter kernel based on a circular area pixel model to improve the underlying frameworks of many state-of-the-art downscalers.
A pixel in a digital camera is the basic element of a sensor and its shape is rectangular. However, a point of light in a scene is spread by an optical system and creates a blurred circular image onto the pixel. In the spatial domain, an optical PSF (Point Spread Function) describes the degree to which an optical system spreads a point of light. Though the PSF from a circular aperture can be expressed by a sombrero function, it is usually modelled with simpler expressions such as a uniform circular disk. Therefore, from the optical point of view, he used a circular area pixel model rather than a rectangular one.
Since his kernel is one-dimensional, the proposed algorithm has two steps: horizontal and vertical processing. For upscaling, an original and a target pixel are treated as circular regions. For downscaling, only target pixel is treated as an elliptical region.
Abundant objective comparisons showed that the proposed downscaling algorithm is the fastest and has the highest PSNR and SSIM values among the commonest non-adaptive image scaling algorithms. Visual comparisons also showed that his algorithm produces the clearest images without blurring and halo effects. His filter kernel can replace the existing spatial kernels of edge-adaptive image downscalers to improve their performance further.
For further information, you can refer to his paper “ A New Image Downscaling Algorithm based on a Circular Area Pixel Model” in “ ACM Conference Proceedings” (EI).
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