Core Concepts
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.
Abstract
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.
Stats
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.