Performance and Energy Consumption Evaluation of the VVC ALF Classification Step Across Different Programming Paradigms and Hardware Platforms

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The Adaptive Loop Filter (ALF) in the Versatile Video Coding (VVC) standard enhances visual quality but introduces a significant computational burden, posing a challenge for its implementation on resource-constrained devices. This paper presents a comprehensive evaluation of the ALF classification step, analyzing its execution time and energy consumption across multiple programming paradigms (scalar, SIMD, CUDA) on both a high-performance desktop and an embedded platform. Results show that on the high-performance desktop, SIMD optimization provides a substantial speedup over the scalar baseline with lower energy consumption, while the discrete GPU’s performance is limited by data transfer overhead. Conversely, the embedded system demonstrates a different landscape: its ARM CPU offers superior energy efficiency when compared to the desktop, and its integrated GPU’s runtime is dominated by kernel execution rather than data transfers. These findings underscore that the optimal ALF implementation is highly platform-dependent, hinging on the specific design trade-offs between processing speed and energy efficiency.