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MSU Video Codecs Comparison 2025
Part 2, 3: FullHD Objective/Subjective

Nineteenth Annual Video-Codecs Comparison by MSU

Video group head:	Dr. Dmitriy Vatolin
Project head:	Dr. Dmitriy Kulikov
Measurements, analysis:	Nickolay Safonov, Aleksandr Kostychev, Anna Bigler, Daniil Kondrashov

compression.ru

Lomonosov
Moscow State University (MSU)
Graphics and Media Lab

Dubna International
State University

MSU Institute of Advanced
Studies of Artificial Intelligence
and Intelligent Systems

News

05.08.2026 Release of the comparison

Results

The places below are given only for quality scores, not taking encoding speed into account
Encoders with scores closer than 1% share one place (YUV-Subjective closer than 5%)

	Slow (1 fps)	Medium (5 fps)	Fast (30 fps)
Best quality (YUV-SSIM 6:1:1)
Best quality (YUV-PSNR avg.MSE 6:1:1)
Best quality (Y-VMAF 0.6.1)
Best quality (Y-VMAF-NEG 0.6.1)
Best quality (YUV-Subjective)

The biggest number of codecs took part in comparison of Slow encoding (1 fps). The winners vary for different objective quality metrics. The participants were rated using BSQ-rate (enhanced BD-rate) scores [1].

[1] A. Zvezdakova, D. Kulikov, S. Zvezdakov, D. Vatolin, "BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions," 2020.

Download and buy report

Free

Enterprise

Number of test sequences

50+

Test video descriptions

Basic codec info

Objective metrics

Only 4 metrics + Subjective

20+ objective metrics

Test videos download

Encoders presets description

PDF report

36 pages

70 pages

HTML report

24 interactive charts

15000+ interacive charts

Price

Free

950 USD

Download/Buy

PDF & HTML reports FullHD Objective & Subjective (ZIP)

You will receive enterprise versions of all 2025 reports (FullHD, Subjective, 4K)

Participated codecs

	Codec name	Use cases	Standard	Version
1	x264 x264 project	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.264/AVC	stable(31e19f9), Linux
2	BILIHEVC Bilibili Inc.	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/HEVC	v4.4.1, Linux
3	homer jcasal-homer	Fast (30 fps)	H.265/HEVC	v0.2, Linux
4	kvazaar kvazaar project	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/HEVC	v2.3.1, Linux
5	Reference x265 MulticoreWare, Inc.	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/HEVC	v4.1, Linux
6	SVT-HEVC Open Visual Cloud	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/HEVC	v1.5.1, Linux
7	Tencent_V265 Tencent	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/HEVC	v1.7.6, Linux
8	turing BBC	Slow (1 fps)	H.265/HEVC	v1.1, Linux
9	xin26x AFather(xin26x)	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/HEVC	v1.0, Windows
10	ANT Ant	Medium (5 fps)	H.266/VVC	v1.25.12.0, Linux
11	BILIVVC Bilibili Inc.	Slow (1 fps), Medium (5 fps)	H.266/VVC	v2.1.1, Linux
12	Taotian S266 Alibaba Taobao	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.266/VVC	v0.2, Linux
13	Tencent266 Tencent	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.266/VVC	v0.4.0, Linux
14	uvg266 Ultra Video Group	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.266/VVC	v0.8.1, Linux
15	VVenC Fraunhofer HHI	Slow (1 fps), Medium (5 fps)	H.266/VVC	v1.13.1, Linux
16	xin26x AFather(xin26x)	Slow (1 fps), Medium (5 fps), Fast (30 fps)	H.265/VVC	v1.0, Windows
17	aom AOMedia	Slow (1 fps), Medium (5 fps)	AV1	AOMedia Project AV1 Encoder v3.12.0, Linux
18	BILIAV1 Bilibili Inc.	Slow (1 fps), Medium (5 fps)	AV1	v3.4.2, Linux
19	rav1e The rav1e contributors	Slow (1 fps), Medium (5 fps)	AV1	v0.8.1, Linux
20	SVT-AV1 Open Visual Cloud	Slow (1 fps), Medium (5 fps)	AV1	v3.0.1, Linux
21	Tencent_TXAV1 Tencent	Slow (1 fps), Medium (5 fps), Fast (30 fps)	AV1	v1.4.4, Linux
22	libvp9 The WebM Project	Slow (1 fps), Medium (5 fps), Fast (30 fps)	VP9	v1.15.2, Linux
23	KwaiVideo Kuaishou Tech.	Slow (1 fps), Medium (5 fps), Fast (30 fps)	private	v1.0, Linux
24	Taotian S26X Alibaba Tmall codec	Slow (1 fps)	private	v1.2, Linux
25	Tencent_TVC Tencent	Slow (1 fps), Medium (5 fps), Fast (30 fps)	private	v0.3.0, Linux
26	TencentAV2 Tencent	Slow (1 fps)	AV2	Linux
27	TencentAVS3 Tencent	Slow (1 fps), Medium (5 fps)	AVS3	v0.3, Linux

Comparison Rules

FullHD codec testing objectives

The main goal of this report is the presentation of a comparative evaluation of the quality of new and existing codecs using objective measures of assessment. The comparison was done using settings provided by the developers of each codec. Nevertheless, we required all presets to satisfy minimum speed requirement on the particular use case. The main task of the comparison is to analyze different encoders for the task of transcoding video – e.g., compressing video for personal use.

Test Hardware Characteristics

CPU: Intel Core i7 12700K (Alder Lake)
SSD: 1Tb
RAM: 4x16GB (64GB)
OS: Windows 11 x64, Ubuntu 22.04 LTS

For this platform we considered three key use cases with different speed requirements:

Fast – 1080p@30fps
Medium – 1080@5fps
Slow – 1080p@1fps

See more on Call For Codecs 2025 page

Videos

Videos for testing set were chosen from MSU video collection via a voting among comparison participants, organizers and an independend expert.

Number of videos in MSU video collection

Year	# FullHD videos	# FullHD samples	# 4K videos	# 4K samples	Total # of videos	Total # of samples
2016	3	7	882	2902	885	2909
2017	1996	4638	1544	4561	3540	9299
2018	4342	10330	1946	5503	6288	15833
2020	4945	12402	2091	6016	7036	18418
2021	4945	12402	2091	6016	7036	18418
2022	7379	19546	2091	6016	9470	25562

Bitrate distribution of videos in MSU video collection Videos bitrate distribution

Final video set consists of 50 sequences including new videos from Vimeo and media.xiph.org derf's collection.

Descriptions of all test videos are presented in a separate PDF provided with the report.

Subjective Comparison Methodology

For subjective quality measurements we used Subjectify.us crowdsourcing platform. We involved 10,800+ participants. After deleting replies from bots we got 529,171 pairwise answers. Bradley-Terry model was used to compute global rank.

To conduct an online crowdsourced comparison, we uploaded encoded streams to Subjectify.us. For better browser compatibility we performed transcoding with x264 and CRF=16.

The platform hired study participants and showed the upload streams to them in pairs. Each pair consisted of two variants of the same test video sequence encoded by various codecs at various bitrates. Videos from each pair were presented to study participant sequentially (i.e., one after another) in full-screen mode. After viewing each pair, participants were asked to choose the video with the best visual quality. They also had the option to play the videos again or to indicate that the videos have equal visual quality. We assigned each study participant 12 pairs, including 2 hidden quality-control pairs, and each received money reward after successfully completing the task. The quality-control pairs consisted of test videos compressed by the x264 encoder at 1 Mbps and 4 Mbps. Responses from participants who failed to choose the 4 Mbps sequence for one or more quality-control questions were excluded from further consideration.

In total we collected 529,171 valid answers from 10,800+ unique participants. To convert the collected pairwise results to subjective scores, we used the Bradley-Terry model [1]. Thus, each codec run received a quality score. We then linearly interpolated these scores to get continuous rate-distortion (RD) curves, which show the relationship between the real bitrate (i.e., the actual bitrate of the encoded stream) and the quality score. Section "RD Curves" shows these curves.

We obtained the subjective scores for this study using Subjectify.us. This platform enables researchers and developers to conduct subjective comparisons of image and video processing methods (e.g., compression, inpainting, denoising, matting, etc.) and carry out studies of human quality perception.

To conduct a study, researchers must apply the methods under comparison to a set of test videos (images), upload the results to Subjectify.us and write a task description for study participants. Subjectify.us handles all the laborious steps of a crowdsourced study: it recruits participants, presents uploaded content in a pairwise fashion, filters out responses from participants who cheat or are careless, analyzes collected results, and generates a study report with interactive plots. Thanks to the pairwise presentation, researchers need not invent a quality scale, as study participants just select the best option of the two.

The platform is optimized for comparison of large video files: it prefetches all videos assigned to a study participant and loads them into his or her device before asking the first question. Thus, even participants with a slow Internet connection wonпїЅt experience buffering events that might affect quality perception.
To try the platform in your research project, reach out to www.subjectify.us. This demo video shows an overview of the Subjectify.us workflow.

Codec Analysis and Tuning for Codec Developers and Codec Users

Computer Graphics and Multimedia Laboratory of Moscow State University:

20+ years working in the area of video codec analysis and tuning using objective quality metrics and subjective comparisons.
30+ reports of video codec comparisons and analysis (H.265, H.264, AV1, VP9, MPEG-4, MPEG-2, decoders' error recovery).
Methods and algorithms for codec comparison and analysis development, separate codec's features and codec's options analysis.

Strong and Weak Points of Your Codec

Deep encoder parts analysis (ME, RC on GOP, mode decision, etc).
Weak and strong points for your encoder and complete information about encoding quality on different content types.
Encoding Quality improvement by the pre and post filtering (including technologies licensing).

Independent Codec Estimation Comparing to Other Codecs for Different Use-cases

Comparative analysis of your encoder and other encoders.
We have direct contact with many codec developers.
You will know place of your encoder between other newest well-known encoders (compare encoding quality, speed, bitrate handling, etc.).

Encoder Features Implementation Optimality Analysis

We perform encoder features effectiveness (speed/quality trade-off) analysis that could lead up to 30% increase in the speed/quality characteristics of your codec. We can help you to tune your codec and find best encoding parameters.