Processing Time Evaluation of the Classification Step in the Adaptive Loop Filter of VVC under Multiple Programming Paradigms

Several tools were introduced by the Versatile Video Coding (VVC) standard to enhance compression, with the Adaptive Loop Filter (ALF) being one such tool that significantly enhances visual quality. Although it provides coding efficiency gains, the ALF also poses a substantial computational burden. To address this issue, this paper evaluates the processing time of the classification step in the ALF process of VVC encoders considering different programming paradigms. A sequential CPU implementation, a Single Instruction Multiple Data implementation, and a customized parallel implementation using CUDA to be executed in GPUs. The results showed that SIMD-optimized implementation significantly outperforms the fully-scalar implementation. Although the GPU paradigm is faster than fully-scalar, it remains slower than SIMD-optimized due to CPU-GPU communication overhead. With more tasks, the GPU could potentially surpass the SIMD-optimized processing time.