FGSVQA

Official Code for the following paper:

X. Wang, A. Katsenou, J.Shen and D. Bull. FGSVQA: Frequency-Guided Short-form Video Quality Assessment

Our paper was accepted by the 18th International Conference on Quality of Multimedia Experience (QoMEX 2026).

Pre-trained checkpoints are on Hugging Face 🤗

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Performance

We validated our proposed method on two publicly available Short-form UGC datasets: KVQ and YouTube SFV+HDR dataset (YT-SFV).

Spearman’s Rank Correlation Coefficient (SRCC)

Model	KVQ	YT-SFV (SDR)	YT-SFV (HDR2SDR)
FGSVQA	0.877	0.788	0.543

Pearson’s Linear Correlation Coefficient (PLCC)

Model	KVQ	YT-SFV (SDR)	YT-SFV (HDR2SDR)
FGSVQA	0.878	0.818	0.666

GPU runtime comparison (averaged over 10 runs) across different spatial resolutions on "SDR_Animal_5ngj.mp4".

Method	Time(s) 540P	Time(s) 720P	Time(s) 1080P	Time(s) 2160P	Ground truth: 4.308 Predicted Score
Fast-VQA	0.599	0.673	0.909	2.217	3.319
FasterVQA	0.489	0.547	0.696	1.343	3.556
DOVER	0.920	1.022	1.293	2.783	3.814
FGSVQA	0.313	0.405	0.697	2.137	3.878

More results can be found in correlation_result.ipynb.

Proposed Model

Overview of the proposed model with the two branches: the frequency-guided weight map and the CLIP vision encoder.

Usage

📌 Install Requirement

The repository is built with Python 3.10 and can be installed via the following commands:

git clone https://github.com/xinyiW915/FGSVQA.git
cd FGSVQA
conda create -n fgsvqa python=3.10 -y
conda activate fgsvqa
pip install -r requirements.txt  

📥 Download UGC Datasets

The corresponding UGC video datasets can be downloaded from the following sources:
KVQ, YouTube SFV+HDR.

The metadata for the experimented UGC dataset is available under ./metadata.

🎬 Test Demo

Run the pre-trained model to evaluate the perceptual quality of a single video. The demo script reports the predicted quality score, runtime, and model complexity.

The model checkpoint should be provided through --ckpt_path. Please use a full checkpoint file, such as qd_model.best.pt, which contains the saved model weights together with the training MOS mean and standard deviation.

To evaluate a single video, run:

python demo_test.py \
    --ckpt_path <MODEL_PATH> \
    --db_path <VIDEO_FOLDER> \
    --video_id <VIDEO_ID> \
    --device <DEVICE>

For example:

python demo_test.py \
    --ckpt_path ./checkpoints/lsvq/qd_model.best.pt \
    --db_path ./test_videos/ \
    --video_id SDR_Animal_5ngj \
    --device cuda

🔁 Cross-Dataset Evaluation

To evaluate a trained model on another dataset, use transfer_test_only.py. This script loads a trained checkpoint, reports the evaluation metrics, and saves the prediction results to a CSV file.

Run:

python transfer_test_only.py \
    --ckpt_path <MODEL_PATH> \
    --csv_path <TEST_METADATA_CSV> \
    --db_path <TEST_VIDEO_FOLDER> \
    --device <DEVICE> \
    --save_pred_csv <SAVE_PREDICTION_CSV>

For example:

python transfer_test_only.py \
    --ckpt_path ./checkpoints/lsvq/qd_model.best.pt \
    --csv_path ./metadata/KVQ_metadata.csv \
    --db_path /path/to/KVQ/videos \
    --device cuda \
    --save_pred_csv /path/to/transfer_test_only_konvid_1k.csv

Training

Steps to train and fine-tune the model on different datasets.

Train Model

Train the model using the metadata CSV file and the corresponding video folder. The metadata CSV file should contain vid and mos columns.

python train.py \
    --csv_path <TRAIN_METADATA_CSV> \
    --db_path <VIDEO_FOLDER> \
    --save_dir <SAVE_DIR> \
    --save_name qd_model.pt \
    --device <DEVICE> \
    --finetune_last_stage

For example:

python train.py \
    --csv_path ./metadata/KVQ_TRAIN_metadata.csv \
    --db_path /path/to/KVQ/videos \
    --save_dir ./checkpoints/kvq \
    --save_name qd_model.pt \
    --device cuda \
    --finetune_last_stage

The script saves the latest checkpoint and the best-performing checkpoint according to the validation SRCC.

Transfer Model

To fine-tune a pre-trained model on a new dataset, run:

python transfer.py \
    --mode finetune \
    --pretrained <PRETRAINED_MODEL_PATH> \
    --csv_path <TARGET_METADATA_CSV> \
    --db_path <TARGET_VIDEO_FOLDER> \
    --save_dir <SAVE_DIR> \
    --save_name transfer.pt \
    --device <DEVICE> \
    --finetune_last_stage

For example:

python transfer.py \
    --mode finetune \
    --pretrained ./checkpoints/shorts-hdr-dataset_sdr/qd_model.best.pt \
    --csv_path ./metadata/KVQ_TRAIN_metadata.csv \
    --db_path /path/to/KVQ/videos \
    --save_dir ./checkpoints_transfer/kvq \
    --save_name transfer.pt \
    --device cuda \
    --finetune_last_stage

Test Only

To directly test a pre-trained model on another dataset, run:

python transfer.py \
    --mode test_only \
    --pretrained <PRETRAINED_MODEL_PATH> \
    --csv_path <TEST_METADATA_CSV> \
    --db_path <TEST_VIDEO_FOLDER> \
    --device <DEVICE>

For example:

python transfer.py \
    --mode test_only \
    --pretrained ./checkpoints/shorts-hdr-dataset_sdr/qd_model.best.pt \
    --csv_path ./metadata/KVQ_metadata.csv \
    --db_path /path/to/KVQ/videos \
    --device cuda

Acknowledgment

This work was funded by the UKRI MyWorld Strength in Places Programme (SIPF00006/1) as part of my PhD study.

Citation

If you find this paper and the repo useful, please cite our paper 😊:

@article{wang2026fgsvqa,
      title={FGSVQA: Frequency-Guided Short-form Video Quality Assessment},
      author={Wang, Xinyi and Katsenou, Angeliki, Shen, Junxiao and Bull, David},
      booktitle={2026 18th International Conference on Quality of Multimedia Experience (QoMEX)}, 
      year={2026},
      organization={IEEE}
}

Contact:

Xinyi WANG, xinyi.wang@bristol.ac.uk