Blind Video Quality Assessment: Exploring the Impact of Sharpness Features

Kernekoncepter

This study explores the effect of sharpness features on Blind Video Quality Assessment (BVQA) models, which aim to predict the perceived quality of videos without access to reference or original videos.

Resumé

The study focuses on exploring the impact of sharpness features on Blind Video Quality Assessment (BVQA) models. Key highlights:

BVQA is an important area of study as video is a crucial component of modern communication, and the rise of mobile technology has led to a wide range of video quality scenarios.

The study uses the CVD2014 dataset, a benchmark dataset for assessing the quality of in-the-wild videos. A subset of 54 videos from the dataset was used due to resource limitations.

The original BVQA model was modified to replace the spatial feature extractor with a sharpness feature extractor. The sharpness feature extractor was pre-trained on the TID2013 dataset to capture sharpness-related distortions.

The performance of the original and modified BVQA models was evaluated using the Spearman Rank Correlation Coefficient (SRCC) and Pearson Linear Correlation Coefficient (PLCC) metrics.

The results show that the modified BVQA model with the sharpness feature extractor performed close to the original model, but did not exceed its performance. This indicates that the sharpness features can be a useful addition to BVQA models, but further optimization may be required.

Future work could involve more extensive training of the sharpness feature extractor, using larger video quality datasets, and exploring ways to integrate sharpness features into the original BVQA model architecture.

Statistik

The study used a subset of 54 videos from the CVD2014 dataset, which contains a total of 234 video sequences with corresponding mean opinion scores (MOS) obtained through subjective quality assessments.

Citater

None.

Vigtigste indsigter udtrukket fra

Study of the effect of Sharpness on Blind Video Quality Assessment

by Anantha Prab... kl. arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05764.pdf

Dybere Forespørgsler

How can the sharpness feature extractor be further optimized to improve its performance in the BVQA model

To further optimize the sharpness feature extractor in the BVQA model, several strategies can be implemented. Firstly, increasing the diversity and complexity of the training data for the sharpness feature extractor can enhance its performance. This can involve incorporating a wider range of sharpness-related distortions in the training dataset to ensure the model learns to extract sharpness features effectively across various scenarios. Additionally, fine-tuning the hyperparameters of the sharpness feature extractor, such as learning rate, batch size, and epochs, can help in optimizing its performance. Experimenting with different architectures or pre-trained models for the sharpness feature extractor can also lead to improvements. Regular evaluation and validation of the sharpness feature extractor on separate datasets can provide insights into its effectiveness and areas for enhancement.

What other types of features, in addition to sharpness, could be explored to enhance the BVQA model's ability to accurately predict video quality

In addition to sharpness, exploring other types of features can significantly enhance the BVQA model's ability to predict video quality accurately. One crucial feature to consider is motion-related features, which can capture aspects like blurring, jerkiness, and temporal consistency in videos. By incorporating motion feature extractors that focus on extracting spatio-temporal information, the model can better assess video quality in dynamic scenes. Furthermore, color-related features can be explored to evaluate color accuracy and vibrancy in videos. Texture features can also play a vital role in assessing the fine details and patterns within video frames. By integrating a diverse set of features like motion, color, and texture alongside sharpness, the BVQA model can offer a more comprehensive and robust evaluation of video quality.

Given the limitations of the CVD2014 dataset used in this study, how could the research be expanded to larger and more diverse video quality datasets to better understand the generalizability of the findings

Expanding the research to larger and more diverse video quality datasets beyond the limitations of the CVD2014 dataset is essential for understanding the generalizability of the findings. One approach could involve leveraging publicly available video quality datasets like KoNViD-1k, LIVE-Qualcomm, and YouTube-UGC, which offer a broader range of videos with varying resolutions, content, and distortions. By training and testing the BVQA model on these extensive datasets, researchers can assess its performance across a more comprehensive set of video scenarios. Additionally, collaborating with other research groups or institutions to access their video quality datasets can provide a more diverse and representative sample for evaluation. Conducting comparative studies across multiple datasets can offer insights into the model's adaptability and effectiveness in real-world video quality assessment scenarios.