Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression.
In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision.
In this paper, we propose a quality enhancement network of versatile video coding (VVC) compressed videos by jointly exploiting spatial details and temporal structure (SDTS).
We analyze the performance of our approach on a variety of CNN architectures and demonstrated FPGA implementation of ResNet18 with our approach results in reduction of around 40% in the memory energy footprint compared to quantized network with negligible impact on accuracy.