Hardware Optimization of Video Quality Improvement Methods Based on Deep Neural Networks
The paper addresses various aspects of optimizing deep video enhancement models for efficient execution on modern hardware. The focus is on a multi-frame generative network with multi-scale structure and frame-by-frame smoothing (MST-GAN). A comprehensive hardware acceleration strategy is proposed, which includes structural thinning, quantization (FP16/INT8), pipeline, parallelization, and model compilation using TensorRT. A comparative analysis is performed before and after optimizations, including changes in FPS, latency, memory consumption, and FLOPs.