Framework for fair objective performance evaluation of single-image super-resolution algorithms

Single-image super-resolution (SISR) is a technology to reconstruct a high-resolution image from a single low-resolution input image. The performance of SISR algorithms is usually evaluated by applying full-reference objective image quality assessment metrics. First, it is argued that the result of objective quality evaluation may become inconsistent with subjective quality assessment, depending on how the input low-resolution image is generated and how up-scaling during SISR is conducted. Since such inconsistency is due to subpixel-level misalignment between the original and output images, a framework is then proposed that compensates any spatial displacement between the two images and enables fair SISR performance evaluation using objective quality metrics.