Video Super-Resolution

The Vimeo-90K is a large-scale high-quality video dataset for lower-level video processing. It proposes three different video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution. Source: https://arxiv.org/pdf/1711.09078.pdf Image Source: http://toflow.csail.mit.edu/

The Vid4 dataset is generally used for testing video super-resolution. It consists of four sequences: walk (740x480, 47 frames), foliage (740x480, 49 frames), city (704x576, 34 frames), and calendar (720x576, 41 frames).

Our dataset was made of videos from MSU Video Upscalers Benchmark Dataset, MSU Video Super-Resolution Benchmark Dataset and MSU Super-Resolution for Video Compression Benchmark Dataset. Dataset consists of real videos (were filmed with 2 cameras), video games footages, movies, cartoons, dynamic ads. How we brought our dataset closer to completeness? * The dataset covers a large number of use cases in the field of SR due to the large number of content types * The dataset contains videos with completely different resolutions, FPS values: 8, 24, 25, 30, 60, as well as high and low spatio-temporal complexity * Distorted videos were obtained using 46 SR methods, some of them were preprocessed with 5 codecs: aomenc, vvenc, x264, x265, uavs3es with different bitrates and qp values * The dataset was manually checked for redundancy Videos from benchmarks are FullHD video crops, since the subjective comparison was made on crops. Therefore, the resolution of all videos in the received dataset is low.

This is a dataset for a video super-resolution task. The dataset contains the most complex content for the restoration task: faces, text, QR-codes, car numbers, unpatterned textures, small details. Videos include different types of motion and different types of degradation: bicubic interpolation (BI) and Gaussian blurring and downsampling (BD). The resolution of all input video sequences is 480x320. Source: https://videoprocessing.ai/benchmarks/video-super-resolution.html Image Source: https://videoprocessing.ai/benchmarks/video-super-resolution.html

The dataset aims to find the algorithms that produce the most visually pleasant image possible and generalize well to a broad range of content. It consists of 30 clips and contains 15 2D-animated segments losslessly recorded from various video games and 15 camera-shot segments from high-bitrate YUV444 sources. The complexity of clips varies significantly in terms of spatial and temporal indexes. Multiple bicubic downscaling mixed with sharpening is used to simulate complex real-world camera degradation. The authors used slight compression and YUV420 conversion to simulate a practical use case. 1920×1080 sources were downscaled to 480×270 input.

VideoLQ consists of videos downloaded from various video hosting sites such as Flickr and YouTube, with a Creative Common license.

A video dataset for benchmarking upsampling methods. Inter4K contains 1,000 ultra-high resolution videos with 60 frames per second (fps) from online resources. The dataset provides standardized video resolutions at ultra-high definition (UHD/4K), quad-high definition (QHD/2K), full-high definition (FHD/1080p), (standard) high definition (HD/720p), one quarter of full HD (qHD/520p) and one ninth of a full HD (nHD/360p). We use frame rates of 60, 50, 30, 24 and 15 fps for each resolution. Based on this standardization, both super-resolution and frame interpolation tests can be performed for different scaling sizes ($\times 2$, $\times 3$ and $\times 4$). In this paper, we use Inter4K to address frame upsampling and interpolation. Inter4K provides both standardized UHD resolution and 60 fps for all of videos by also containing a diverse set of 1,000 5-second videos. Differences between scenes originate from the equipment (e.g., professional 4K cameras or phones), lighting conditions, variations in movements, actions or objects. The dataset is divided into 800 videos for training, 100 videos for validation and 100 videos for testing.

This is a dataset for a super-resolution task. The dataset contains 480x270 videos that were decoded with 6 different bitrates (100 - 4000 kbps) using 5 different codecs (H.264, H.265, H.266, AV1, and AVS3 standards). The dataset contains indoor and outdoor videos as well as animation. All videos have low SI/TI values and simple textures. It was made to minimize compression artifacts that may occur to make restoration of details possible.

Our RealMCVSR dataset provides real-world HD video triplets concurrently recorded by Apple iPhone 12 Pro Max equipped with triple cameras having fixed focal lengths: ultra-wide (30mm), wide-angle (59mm), and telephoto (147mm). To concurrently record video triplets, we built an iOS app that provides full control over exposure parameters (i.e., shutter speed and ISO) of the cameras. For recording each scene, we set the cameras in the auto-exposure mode, where the shutter speeds of the three cameras are synced to avoid varying motion blur across a video triplet. ISOs are adjusted accordingly for each camera to pick up the same exposure. Each video is saved in the MOV format using HEVC/H.265 encoding with the HD resolution (1080 x 1920). The dataset contains triplets of 161 video clips with 23,107 frames in total. The video triplets are split into training, validation, and testing sets, each of which has 137, 8, and 16 triplets with 19,426, 1,141, and 2,540 frames, respectively.

QST contains 1,167 video clips that are cut out from 216 time-lapse 4K videos collected from YouTube, which can be used for a variety of tasks, such as (high-resolution) video generation, (high-resolution) video prediction, (high-resolution) image generation, texture generation, image inpainting, image/video super-resolution, image/video colorization, image/video animating, etc. Each short clip contains multiple frames (from a minimum of 58 frames to a maximum of 1,200 frames, a total of 285,446 frames), and the resolution of each frame is more than 1,024 x 1,024. Specifically, QST consists of a training set (containing 1000 clips, totally 244,930 frames), a validation set (containing 100 clips, totally 23,200 frames), and a testing set (containing 67 clips, totally 17,316 frames). Click here (Key: qst1) to download the QST dataset.

SEPE 8K dataset is made of 40 different 8K (8192 x 4320) video sequences and 40 variant 8K (8192 x 5464) images. The video sequences were captured at a framerate of 29.97 frames per second (FPS) and had been encoded into videos using AVC/H.264, HEVC/H.265, and AV1 codecs at resolutions from 8K to 480p. The images, video sequences, encoded videos, and various other statistics related to the media that make the dataset are stored online, published, and maintained on the repo on GitHub for non-commercial use. this proposed dataset is - as far as we know - the first to publish true 8K natural sequences; thus, it is important for the next level of applications dealing with multimedia such as video quality assessment, super-resolution, video coding, video compression, and many more. Paper: https://doi.org/10.1145/3587819.3592560

SAT-MTB-VSR is a large-scale dataset for satellite video super-resolution made from original videos of Jilin-1, which is a subset of the satellite video multitasking dataset SAT-MTB. The dataset is cropped from 18 videos captured by the Jilin-1 video satellite, covering a wide range of terrains, such as cities, docks, airports, suburbs, forests, and deserts, with a resolution of about 1 m. And the videos contain dynamic scenes, such as moving cars, airplanes, trains, and ships, which test the ability of the VSR method to deal with moving targets of different sizes and speeds. At the same time, due to the motion of the satellite, the video contains changes in viewing angle and lighting. It contains 431 videos, each of which is 100 consecutive frames, of which 413 are used as the training set and 18 as the validation set, and all the 18 validation sets are from different original videos, while the size of the images is 640 × 640. These images are downsampled 4× by bicubic interpolation to get 160 × 160 low-resolution images, thus obtaining the LR-HR training pairs.

To evaluate the performance on 4K burst images/video, we collect several clips from website. The dataset can be download from : https://drive.google.com/file/d/1YDljUONvyKUO24smTx__CUH_4Zxhle09/view?usp=sharing