Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of internet communication and cloud service technology, video content and traffic are growing exponentially, which further emphasizes the requirement for accurate and rapid assessment of video quality. Therefore, numerous subjective and objective video quality assessment studies have been conducted over the past two decades for both generic videos and specific videos such as streaming, user-generated content (UGC), 3D, virtual and augmented reality (VR and AR), high frame rate (HFR), audio-visual, etc. This survey provides an up-to-date and comprehensive review of these video quality assessment studies. Specifically, we first review the subjective video quality assessment methodologies and databases, which are necessary for validating the performance of video quality metrics. Second, the objective video quality assessment algorithms for general purposes are surveyed and concluded according to the methodologies utilized in the quality measures. Third, we overview the objective video quality assessment measures for specific applications and emerging topics. Finally, the performances of the state-of-the-art video quality assessment measures are compared and analyzed. This survey provides a systematic overview of both classical works and recent progresses in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.
Expert Commentary: Video Quality Assessment in the Era of Multimedia Information Systems
Video quality assessment is a crucial area within the field of multimedia information systems, which encompasses various aspects of video processing and delivery. As mentioned in the article, the increasing demand for video content and the growth of internet communication highlight the need for accurate and rapid assessment of video quality. This is particularly important due to the presence of quality degradations that occur during different stages of video signal acquisition, compression, transmission, and display.
One noteworthy aspect of video quality assessment is its multidisciplinary nature. It encompasses concepts from diverse fields such as video processing, human perception, signal processing, and data analysis. By integrating knowledge from these disciplines, researchers have conducted both subjective and objective video quality assessment studies over the past two decades.
Subjective Video Quality Assessment
The first aspect explored in this survey is subjective video quality assessment methodologies and databases. Subjective assessment involves human observers who rate the quality of videos based on their visual experience. This approach is essential for validating the performance of objective video quality metrics. Several databases have been created, containing videos with various perceptual characteristics and degradation types. These databases serve as valuable resources for evaluating video quality algorithms.
Objective Video Quality Assessment
The next focus of the survey is on objective video quality assessment algorithms for general purposes. Objective assessment aims to develop computational models that can predict perceived video quality without the need for human judgments. These algorithms utilize different methodologies such as machine learning, statistical analysis, and mathematical models to estimate video quality. The survey provides an overview of these algorithms, allowing researchers to understand their strengths and limitations.
Video Quality Assessment for Specific Applications
As video applications evolve, it becomes crucial to develop objective quality assessment measures tailored to specific contexts. This survey covers objective video quality assessment measures for emerging topics such as streaming, user-generated content (UGC), 3D, virtual and augmented reality (VR and AR), high frame rate (HFR), and audio-visual videos. Each of these applications poses unique challenges, and the survey highlights the state-of-the-art measures employed in these domains.
Analyzing State-of-the-Art Measures
Finally, the survey compares and analyzes the performances of state-of-the-art video quality assessment measures. This analysis helps researchers gauge the effectiveness of different algorithms and identify areas for improvement. By understanding the strengths and weaknesses of existing measures, researchers can strive to develop more accurate and robust video quality assessment techniques.
In summary, this comprehensive survey provides a systematic overview of video quality assessment in the field of multimedia information systems. It covers subjective and objective assessment methodologies, explores specific applications, and compares the performances of state-of-the-art measures. This valuable resource enables researchers to access the field quickly and conduct relevant research, thus contributing to the advancement of video quality assessment in various domains like animations, artificial reality, augmented reality, and virtual realities.