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README.md

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----
-pretty_name: LEHA-CVQAD
-tags:
-- video
-- computer-vision
-- compression
-- video-quality-assessment
-- subjective-quality
-- benchmark
-size_categories:
-- 1K<n<10K
-license: apache-2.0
----
-
-# Dataset Card for LEHA-CVQAD
-
-## Dataset Summary
-
-LEHA-CVQAD is a large-scale dataset for **compressed video quality assessment**. It is designed for benchmarking and training both **full-reference (FR)** and **no-reference (NR)** video quality assessment methods on modern compression artifacts.
-
-The dataset combines:
-- **diverse source content**, including both professionally produced material and user-generated content,
-- **modern compression standards and codec presets**,
-- **pairwise preference annotations** converted into ranking scores,
-- **MOS / DMOS annotations** on a selected subset,
-- and a public **open split** plus a **hidden split** used for blind benchmark evaluation.
-
-This repository contains the **public open part** of the dataset. The hidden part is not released publicly and is used for benchmark evaluation to reduce overfitting.
-
-- Paper: https://arxiv.org/abs/2507.03990
-- Earlier dataset / methodology paper: https://arxiv.org/abs/2211.12109
-- Benchmark page: https://videoprocessing.ai/benchmarks/video-quality-metrics.html
-- Dataset / project page: https://videoprocessing.ai/datasets/cvqad.html
-
-## Supported Tasks and Leaderboards
-
-This dataset can be used for:
-
-1. **No-reference video quality assessment**
-   - Predict perceptual quality from a distorted video alone.
-
-2. **Full-reference video quality assessment**
-   - Predict perceptual quality from a distorted video and its pristine reference.
-
-3. **Pairwise ranking / preference learning**
-   - Learn relative quality ordering between compressed variants of the same source content.
-
-4. **Quality regression**
-   - Predict MOS, DMOS, Bradley-Terry scores, Elo scores, or a fused subjective score.
-
-5. **Codec / rate-distortion optimization research**
-   - Study how objective metrics align with human preference under bitrate constraints.
-
-Benchmark results for many IQA/VQA metrics are reported on the MSU benchmark website.
-
-## Languages
-
-The dataset is visual. Spoken language is not a primary annotation axis.
-
-## Dataset Structure
-
-### Data Instances
-
-A typical dataset instance represents one compressed video and its metadata.  
-Replace field names below with the exact keys used in your CSV / JSON metadata.
-
-```json
-{
-  "id": "leha_cvqad_000001",
-  "reference_id": "src_012",
-  "distorted_video": "distorted/codec_xxx/video_000001.mp4",
-  "reference_video": "references/src_012.y4m",
-  "split": "open",
-  "source_type": "raw_or_ugc",
-  "content_category": "sports",
-  "codec_family": "hevc",
-  "codec_name": "x265",
-  "preset": "medium",
-  "target_bitrate_kbps": 2000,
-  "width": 1920,
-  "height": 1080,
-  "fps": 30,
-  "bt_score": 0.73,
-  "elo_score": 1462.1,
-  "mos": 13.6,
-  "dmos": 5.2,
-  "fused_score": 0.69
-}
-````
-
-### Data Fields
-
-Use this section to describe your actual metadata schema. A typical release may contain:
-
-* `id`: unique sample identifier
-* `reference_id`: identifier of the source video
-* `distorted_video`: path or filename of the compressed video
-* `reference_video`: path or filename of the reference video for FR evaluation
-* `split`: dataset split, usually `open`
-* `source_type`: whether the source content is pristine / professional or UGC
-* `content_category`: coarse content label, if provided
-* `codec_family`: compression standard family (for example AVC, HEVC, VVC, AV1, VP9)
-* `codec_name`: concrete encoder / codec implementation
-* `preset`: encoding preset
-* `target_bitrate_kbps`: target bitrate used during encoding
-* `width`, `height`, `fps`: technical properties of the sample
-* `bt_score`: Bradley-Terry subjective ranking score
-* `elo_score`: Elo-based subjective ranking score
-* `mos`: mean opinion score
-* `dmos`: differential mean opinion score
-* `fused_score`: unified score derived from pairwise and rating experiments
 
-### Data Splits
+# MSU Compression Dataset Description
 
-The full LEHA-CVQAD benchmark is divided into:
+<br />
 
-* **Open split:** 1,963 videos
-* **Hidden split:** 4,277 videos
+We developed LEHA-CVQAD dataset to evaluate full-reference and no-reference video quality metrics. Here we share the open part of the whole compression artifacts dataset (1,962 out of 6,240 videos). The hidden part is only available to benchmark-support personnel for testing metric performance. All videos are of *mostly* FullHD resolution, YUV420, and 10-15 seconds duration. Fps values are 24, 25, 30, 39, 50, and 60.
 
-The full benchmark contains 6,240 distorted videos in total.
-This Hugging Face repository releases the **open split only**.
+Subjective quality scores are also provided in csv file. The higher the score is the better is the quality. To study more about the subjective quality evaluation procedure of our benchmark, you can visit the FAQ section at [Subjectify.us](https://www.subjectify.us). 
 
-## Dataset Creation
+Also, a more detailed description of the dataset and benchmark methodology can be found at the paper TODO.
 
-### Curation Rationale
+Leaderboard of more than 100 metrics on LEHA-CVQAD dataset: [MSU Video Quality Metrics Benchmark page](https://videoprocessing.ai/benchmarks/video-quality-metrics.html).
 
-The dataset was created to support **generalized video quality assessment of compression artifacts**. Existing public datasets often have one or more of the following limitations:
+<br />
 
-* limited codec diversity,
-* limited content diversity,
-* lack of authentic UGC artifacts,
-* small scale,
-* or subjective labels that are hard to compare across sources.
+## Dataset Folder Structure
 
-LEHA-CVQAD was designed to address these issues by combining diverse source content, a wider range of codec presets, and a richer subjective annotation protocol.
+<br />
 
-### Source Data
+* **Subjective_scores_and_videos_info.csv** contains subjective scores (MOS, Bradley-Terry, ELO) for each compressed video. Each distorted video beside its subjective quality has the following characteristics:
+	* *name of the original (pristine) video*
+	* *codec used for encoding*
+	* *codec standard (avc, hevc, vvc, av1, ...)*
+	* *target bitrate or crf*
+	* *bitrate range (high, mid, low)*
+	* *original video resolution*
+	* *original video fps*
 
-#### Initial source video extraction
+<br />
 
-The source-video extraction methodology follows the earlier CVQAD work. In that pipeline, high-bitrate open-source videos were collected from Vimeo, filtered by license and bitrate, converted to YUV 4:2:0, and sampled using spatial/temporal complexity clustering to obtain a representative and diverse set of reference videos.
+* **Metrics_scores.csv** contains 100+ VQA metrics values on our dataset and can be used to calculate VQA metrics correlations with subjective scores
 
-#### LEHA-CVQAD extension
+* **Compressed_and_GT_videos** contains 59 folders, each of which include 1 *reference videos* (GT), which is required to test full-reference metrics, and many *distorted videos* (compressed), grouped by encoding preset:
 
-LEHA-CVQAD extends this approach by collecting a larger candidate pool of FullHD pristine videos from Vimeo and Xiph, adding more UGC content, and sampling a final set of **59** reference videos through complexity-aware clustering and manual selection for genre diversity.
+	* ``Each distorted video has the following pattern: {video name}/{encoding preset}/{codec name}_{crf or bitrate}.mp4``
 
-The final source set includes categories such as:
+	* ``Each reference video has the following pattern: {video name}/GT.mp4``
 
-* sports,
-* gaming,
-* nature,
-* interviews / television clips,
-* animation,
-* vlogs,
-* advertisements,
-* music videos,
-* water surfaces,
-* face close-ups,
-* and UGC.
+<br />
 
-### Data Collection and Processing
+## Correlation Calculation for MOS
 
-Each source video was compressed using a broad range of codecs and presets to cover modern compression artifacts. The full benchmark uses:
+The following pipeline should be applied only to calculate correlation between metrics scores and **MOS** subjective scores. 
 
-* **186 codec / preset variants**
-* **3 target bitrates:** 1000, 2000, and 4000 kbps
-* multiple compression standards including AVC, HEVC, VVC, VP9, AV1, and others
+Just apply single correlation coefficient to the **whole list** of MOS subjective scores and metrics scores.
 
-Not every source video was encoded with every available codec.
 
-The public benchmark design separates:
+## Correlation Calculation for BT and ELO
 
-* **open-source codec outputs** into the public split,
-* and **proprietary codec outputs** into the hidden split used for blind evaluation.
+<br />
 
-### Annotations
+The following pipeline should be applied only to calculate correlation between metrics scores and **BT and ELO** subjective scores. 
 
-LEHA-CVQAD provides two kinds of subjective labels:
+There are 59 different original (pristine) videos, as well as several encoding presets in the dataset. **Please pay attention: It is required to calculate the correlation coefficient (SRCC, KRCC, ...) on all of them SEPARATELY**. Therefore, to get a single correlation for the whole dataset, you should use Fisher Z-transform to average group correlations weighted proportionally to group size as follows:
 
-1. **Pairwise rankings**
+<br />
 
-   * Viewers compare two compressed videos derived from the same source and choose the better one or mark them as equivalent.
-   * These comparisons are converted into subjective ranking scores using **Bradley-Terry** and **Elo** models.
+1) Iterate through 59 original videos and for each calculate correlation coefficients, as many times as the quantity of unique presets for the current video (i.e. for basketball-2021 with 2 presets *fast* and *offline* you should obtain 2 correlations)
 
-2. **MOS / DMOS labels**
+<br />
 
-   * A subset of videos is rated with **Absolute Category Rating (ACR)** on a **21-point scale**.
-   * Reference videos are included to support DMOS computation.
+2) Use the inverse hyperbolic tangent (artanh) on each value of the correlation coefficient
+	* Replace possible infinity with artanh(0.99)  
+	
+<br />
 
-The final dataset uses a fusion procedure to combine pairwise and rating information into a more globally consistent quality scale.
+3) Apply weighted arithmetic mean to obtained values. For example, if $SROCC_1$ is the spearman correlation counted for the group of samples of size $Size_1$, $SROCC_2$ is the spearman correlation counted for the group of samples of size $Size_2$, then the final correlation have to be counted as $\frac{SROCC_1 * SIZE_1 + SROCC_2 * SIZE_2}{SIZE_1 + SIZE_2}$.
 
-### Subjective Study Protocol
+<br />
 
-All subjective data were collected through a crowdsourcing platform in a browser-based full-screen setting. Videos were shown at FullHD resolution and pre-buffered before playback. Verification questions and participant filtering were used for quality control.
+4) Calculate the hyperbolic tangent (tanh) of the weighted mean
+	* Take the absolute value of it and replace 0.99 with 1
 
-For pairwise comparisons:
+<br />
 
-* each task contained 12 videos presented as pairs,
-* two comparisons were verification questions,
-* at least 10 valid responses were collected for each pair.
+5) The obtained value represents the correlation between your method scores and the subjective scores on our dataset.
 
-For MOS collection:
+<br />
 
-* participants completed training before rating,
-* quality control filtered inconsistent or low-effort responses.
+Script to calculate metrics correlations with subjective scores (BT and ELO) is provided in the GitHub repo: https://github.com/msu-video-group/MSU_VQM_Compression_Benchmark
 
-### Statistics
 
-For the full LEHA-CVQAD benchmark:
 
-* **59** source videos
-* **6,240** distorted videos
-* **1,963** videos in the public open split
-* **4,277** videos in the hidden split
-* **186** codec / preset variants
-* approximately **1,797,310** valid pairwise responses
-* more than **15,000** unique participants in pairwise experiments
-* MOS responses collected from **1,496** participants
-
-## Dataset Use
-
-### Direct Use
-
-The dataset is suitable for:
-
-* training NR-VQA models,
-* training FR-VQA models,
-* metric benchmarking,
-* pairwise ranking models,
-* regression models for perceptual quality,
-* and research on codec optimization and rate-distortion alignment.
-
-### Out-of-Scope Use
-
-The dataset is not intended for:
-
-* general video understanding,
-* semantic recognition,
-* action recognition,
-* captioning,
-* or speech / language tasks.
-
-It should also not be treated as a universal proxy for all video distortions, since it is specifically oriented toward **compression-related quality assessment**.
-
-## Bias, Risks, and Limitations
-
-Several limitations should be considered:
-
-* The public release is only the **open split**. Results on this split alone may overestimate generalization compared with blind benchmark evaluation.
-* The dataset focuses on **compression artifacts** and is less suitable for unrelated distortions such as camera shake, defocus, or transmission artifacts unless they are already present in the source content.
-* Subjective studies were crowd-based rather than fully controlled laboratory studies.
-* Some content categories, codecs, or bitrate regions may be easier for current metrics than others.
-* Pairwise labels are naturally local to variants of the same source; the fused scale reduces but may not eliminate all cross-content comparability issues.
-
-## Data Preprocessing
-
-Typical preprocessing for research use may include:
-
-* decoding compressed videos to frames,
-* temporal subsampling,
-* patch or clip extraction,
-* normalization of subjective labels,
-* and pairing distorted videos with reference videos for FR evaluation.
-
-Researchers should report preprocessing choices clearly for reproducibility.
-
-## Evaluation
-
-Common evaluation protocols include:
+---
+---
 
-* Spearman rank correlation coefficient (SRCC)
-* Pearson linear correlation coefficient (PLCC)
-* Kendall rank correlation coefficient (KRCC)
-* pairwise ranking accuracy
-* codec-wise or bitrate-wise subgroup evaluation
 
-When reproducing benchmark results, users should ensure that they do not train on hidden benchmark content.
+## Encoding and Decoding
 
-## Citation
 
-If you use this dataset, please cite the LEHA-CVQAD paper and the earlier CVQAD methodology paper:
+<br />
 
-```bibtex
-@article{gushchin2025leha,
-  title={LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts},
-  author={Gushchin, Alexander and Smirnov, Maksim and Antsiferova, Anastasia and others},
-  journal={arXiv preprint arXiv:2507.03990},
-  year={2025}
-}
+* To encode videos we used the following command:
+```
+	ffmpeg −f rawvideo −vcodec rawvideo −s {width}x{height} −r {FPS} −pix_fmt yuv420p −i {video name}.yuv −c:v libx265 −x265−params "lossless =1:qp=0" −vsync 0 {video name}.mp4
 ```
 
-```bibtex
-@inproceedings{antsiferova2022video,
-  title={Video compression dataset and benchmark of learning-based video-quality metrics},
-  author={Antsiferova, Anastasia and Lavrushkin, Sergey and Smirnov, Maksim and Gushchin, Alexander and Vatolin, Dmitriy and Kulikov, Dmitriy},
-  booktitle={NeurIPS 2022 Datasets and Benchmarks Track},
-  year={2022}
-}
+* To decode the video back to YUV you can use:
+```
+	ffmpeg -i {video name}.mp4 -pix_fmt yuv420p -vcodec rawvideo -f rawvideo {video name}.yuv
+```
+* To convert the encoded video to the set of PNG images you can use:
 ```
+	ffmpeg -i {video name}.mp4 {frames dir}/frame_%05d.png
+```
+<br />
+
+
+## Support and maintaining
 
-## Additional Information
+<br />
 
-This repository corresponds to the public dataset release.
-For blind evaluation on the hidden split and benchmark results for existing metrics, see the MSU benchmark pages linked above.
+The CMC MSU Graphics and Media Lab hosts the dataset. The team that works with codecs and video quality assessment methods maintains it. Also, the authors of this paper support the video quality metrics benchmark. If you have any question regarding the usage of LEHA-CVQAD, please feel free to contact us via vqa@videoprocessing.ai