Methodology and annual comparisons of video codecs and cloud transcoding services.
The MSU Video Codecs Comparison is a long-running series of independent evaluations of video encoders (Vatolin et al., 2019), extended to cloud transcoding services (Vatolin et al., 2019).
To make such comparisons reliable, we developed BSQ-rate, a new approach to video codec performance comparison that addresses drawbacks of BD-rate (Zvezdakova et al., 2020), studied which objective quality metrics best predict subjective quality in codec comparisons (Antsiferova et al., 2021), and built a shot boundary detection method with a new extensive dataset (Gushchin et al., 2021).
References
2021
GraphiCon
Applying objective quality metrics to video-codec comparisons: Choosing the best metric for subjective quality estimation
Anastasia Antsiferova, Alexander Yakovenko, Nickolay Safonov, Dmitriy Kulikov, Alexander Gushchin, and Dmitriy Vatolin
Quality assessment is essential to creating and comparing video compression algorithms. Despite the development of many new quality-assessment methods, well-known and generally accepted codecs comparisons mainly employ classical methods such as PSNR, SSIM, and VMAF. These methods have different variations: temporal pooling techniques, color-component summations and versions. In this paper, we present comparison results for generally accepted video-quality metrics to determine which ones are most relevant to video codecs comparisons. For evaluation we used videos compressed by codecs of different standards at three bitrates, and subjective scores were collected for these videos. Evaluation dataset consists of 789 encoded streams and 320294 subjective scores. VMAF calculated for all Y, U, V color spaced showed the best correlation with subjective quality, and we also showed that the usage of smaller weighting coefficients for U and V components leads to a better correlation with subjective quality.
@article{vatolinApplying2021,title={{Applying objective quality metrics to video-codec comparisons: Choosing the best metric for subjective quality estimation}},author={Antsiferova, Anastasia and Yakovenko, Alexander and Safonov, Nickolay and Kulikov, Dmitriy and Gushchin, Alexander and Vatolin, Dmitriy},year={2021},journal={GraphiCon},}
GraphiCon
Shot boundary detection method based on a new extensive dataset and mixed features
Alexander Gushchin, Anastasia Antsiferova, and Dmitriy Vatolin
In 31st International Conference on Computer Graphics and Vision (GraphiCon 2021), 2021
Shot boundary detection in video is one of the key stages of video data processing. A new method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed. The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2] datasets, and the MSU CC datasets, based on videos used in the video codec comparison conducted at MSU, as well as videos from the IBM set, were also plotted. The total dataset for algorithm development and testing exceeded the known TRECVID datasets. Based on the test results, the proposed algorithm for scene change detection outperformed its counterparts with a final F-score of 0.9794.
@inproceedings{vatolinShot2021,title={{Shot boundary detection method based on a new extensive dataset and mixed features}},author={Gushchin, Alexander and Antsiferova, Anastasia and Vatolin, Dmitriy},year={2021},booktitle={31st International Conference on Computer Graphics and Vision (GraphiCon 2021)},}
2020
PCS
BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions
Anastasia V Zvezdakova, Dmitriy L Kulikov, Sergey V Zvezdakov, and Dmitriy S Vatolin
This paper is dedicated to the analysis of the existing approaches to video codecs comparisons. It includes the revealed drawbacks of popular comparison methods and proposes new techniques. The performed analysis of user-generated videos collection showed that two of the most popular open video collections from media.xiph.org which are widely used for video-codecs analysis and development do not cover real-life videos complexity distribution. A method for creating representative video sets covering all segments of user videos the spatial and temporal complexity is also proposed. One of the sections discusses video quality estimation algorithms used for video codec comparisons and shows the disadvantages of popular methods VMAF and NIQE. Also, the paper describes the drawbacks of the BD-rate – generally used method for video codecs final ranking during comparisons. A new ranking method …
@article{vatolinBsqrate2020,title={{BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions}},author={Zvezdakova, Anastasia V and Kulikov, Dmitriy L and Zvezdakov, Sergey V and Vatolin, Dmitriy S},year={2020},journal={Programming and computer software},}
2019
Report
Msu video codecs comparison
D Vatolin, D Kulikov, M Erofeev, and A Antsiferova
@article{antsiferovaVideo2019,title={{Video transcoding clouds comparison 2019}},author={Vatolin, Dmitriy and Kulikov, Dmitriy and Sklyarov, Egor and Zvezdakov, Sergey and Antsiferova, Anastasia},year={2019},journal={Moscow State University, Tech. Rep},}