DIV2K is a popular single-image super-resolution dataset which contains 1,000 images with different scenes and is splitted to 800 for training, 100 for validation and 100 for testing. It was collected for NTIRE2017 and NTIRE2018 Super-Resolution Challenges in order to encourage research on image super-resolution with more realistic degradation. This dataset contains low resolution images with different types of degradations. Apart from the standard bicubic downsampling, several types of degradations are considered in synthesizing low resolution images for different tracks of the challenges. Track 2 of NTIRE 2017 contains low resolution images with unknown x4 downscaling. Track 2 and track 4 of NTIRE 2018 correspond to realistic mild ×4 and realistic wild ×4 adverse conditions, respectively. Low-resolution images under realistic mild x4 setting suffer from motion blur, Poisson noise and pixel shifting. Degradations under realistic wild x4 setting are further extended to be of different
280 PAPERS • 3 BENCHMARKS
Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test.
195 PAPERS • 8 BENCHMARKS
Quality Assessment research strongly depends upon subjective experiments to provide calibration data as well as a testing mechanism. After all, the goal of all QA research is to make quality predictions that are in agreement with subjective opinion of human observers. In order to calibrate QA algorithms and test their performance, a data set of images and videos whose quality has been ranked by human subjects is required. The QA algorithm may be trained on part of this data set, and tested on the rest.
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Five classic grayscale images commonly used for image quality assessment tasks.
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