Hardware Acceleration of Video Quality Metrics

SPIE Optics + Photonics

Abstract

Quality Metrics (QM) provide an objective way to measure perceived video quality. These metrics are very compute intensive and are currently done in software. In this paper, we propose an accelerator that can compute metrics like single scale and multi-scale Structural Similarity Index (SSIM, MS_SSIM) and Visual Information Fidelity (VIF). The proposed accelerator offers an energy efficient solution compared to traditional CPUs. It improves memory bandwidth utilization by computing multiple Quality metrics simultaneously.

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