Our benchmark features the most extensive comparison of existing image and video quality metrics relative for Super-Resolution task and determines the best of them. Everyone is welcome to participate!

Our benchmark determines the best upscaling methods for increasing video resolution and improving visual quality using our compact yet comprehensive dataset and features the most extensive comparison of video super-resolution (VSR) algorithms by subjective quality. Everyone is welcome to participate! Run your favorite super-resolution method on our compact test video and send us the result to see how well it performs.

With the emergence of new video resolution standards, more efficient video encoding and decoding techniques are required. Our benchmark can help determine the best SR models to work with each of the different codec standards. This information will help make video coding with downsampling more effective.

Super-Resolution is the process of calculating high-resolution samples from their low-resolution counterparts. Working with images we can utilize natural preferences and make a high-resolution image, which is only in a way similar to the real one. Our benchmark is aimed to find the best algorithms for the restoration of real details with Video Super-Resolution.

Our benchmark evaluates the best video colorization methods for color propagation and automatic colorization.
It is the first benchmark for the video colorization task with domain specific dataset and collected MOS. Everyone is welcome to participate!

Our benchmark is a comparison of the best learned and conventional image compression methods. Last years learning based image compression methods show expressive results in Rate-Distortion performance. Our benchmark is the first large comparison of learned image compression methods. Everyone welcome to participate!

Our benchmark is a comparison of the best video saliency prediction methods for recognition of the most important areas of the video. It is based on a high-resolution multitype dataset collected from observers using eye-tracker. The comparison methodology includes Domain adaptation with brightness change and Center Prior blending for generalization of blurring of initial fixations.

Image and video quality assessment plays a key role in optimal media compression. Neural-network-based methods show higher performance than traditional methods, however they also became more vulnerable to adversarial attacks, that increase the metric without improving visual quality. We collected more than 15 IQA/VQA methods, adapted adversarial attacks on classifiers, and compared methods' robustness.

Deblurring is the process of removing blurring artifacts from images. Video deblurring recovers a sharp sequence from a blurred one. Current SOTA aproaches use deep learning algorithms for this task. Our benchmark ranks these algorithms and determines which is the best by means of restoration quality.

Video-quality measurement is a critical task in video processing. We present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of 2500+ streams encoded using different standards, including AVC, HEVC, AV1, and VVC. The list of evaluated metrics includes recent ones based on machine learning and neural networks.

Video-quality measurement is a critical task in video processing. We present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of 2500+ streams encoded using different standards, including AVC, HEVC, AV1, and VVC. The list of evaluated metrics includes recent ones based on machine learning and neural networks.

HDR restoration is the process of creating an HDR video from its SDR version by restoring brightness. Our benchmark evaluates the quality of HDR video recovery from SDR using various algorithms. This benchmark will help you find the method with the most natural restoration of HDR video.

Measurement of the speed and power efficiency of different codecs on different mobile platforms allows for deeper understanding of their suitability for different devices, and allows manufacturers to fine-tune their codec integration

One of the basic steps in video processing is video scene splitting. For example, scene cutting is a necessary step in video annotation and indexing, keyframe searching, and automatic video format changing. Our benchmark is aimed at measuring the performance of video scene splitting algorithms

Deinterlacing is the process of converting interlaced video into a non-interlaced or progressive form. Interlaced video signals are commonly found in analog television, digital television, some DVD titles, and a smaller number of Blu-ray discs. Our benchmark is aimed at measuring the performance of video deinterlacing algorithms

Often, broadcasted video sequences can have some freeze frames. Because of this, the process of comparing the initial sequence and the result one is very obstructed. Video alignment aims at finding point correspondences between two video sequences to overcome this problem. Our benchmark is aimed at measuring the performance of video alignment algorithms

The Video­Mat­ting pro­ject is the first pub­lic ob­jec­tive bench­mark for video-mat­ting meth­ods. We be­lieve our work will help rank ex­ist­ing meth­ods and aid de­vel­op­ers of new meth­ods in im­prov­ing their re­sults.

The Video­Com­ple­tion pro­ject in­tro­duces the first bench­mark for video-com­ple­tion meth­ods. We pre­sent re­sults for dif­fer­ent meth­ods on a range of di­verse test se­quences which are avail­able for view­ing on a player equipped with a mov­able zoom re­gion. We be­lieve that our work can help rank ex­ist­ing meth­ods and as­sist de­vel­op­ers of new gen­eral-pur­pose video-com­ple­tion meth­ods.

Video Frame Interpolation Benchmark

A low frame rate causes aliasing, yields abrupt motion artifacts, and degrades the video quality. To solve this problem a lot of video frame interpolation algorithms have been created so far. Our benchmark will rank these algorithms and determine which is the best by means of interpolation quality.