Energy Efficiency of Video Quality Assessment Metrics

Video Quality Assessment (VQA) metrics play a crucial role in modern video processing systems, yet their computational costs and environmental impact have received limited attention. This paper presents a comprehensive analysis of state-of-the-art VQA metrics across two standard datasets, including processing time, memory usage, and the ability to predict subjective scores. Results demonstrate that full-reference video metrics achieve superior accuracy compared to image-based metrics such as SSIM, but require up to more than 20 times more energy on average. No-reference metrics offer even higher accuracy but substantially larger energy usage. This difference in computational requirements has significant implications for energy consumption and environmental impact, particularly in large-scale deployments or high-resolution video processing scenarios.