Abstract: A method and system for video encoding systems guided by hybrid visual attention analysis. The method comprises takes video frames as inputs and generate two saliency maps from bottom-up and top-down attention analysis respectively. The saliency maps are combined into a unified map used to conduct bit allocation in video encoding systems to reduce encoded video bandwidth while at the same time preserving the same or even better visual quality.

Abstract: A method and system for allocating video scaling resources in a deep learning model across a communication network. The method comprises estimating a first set of layers of the deep learning model for downscaling of a video content stream generated at a video server of the communication network; estimating a second set of layers of the deep learning model for upscaling of the video content stream at a rendering device of the communication network; and allocating resources amongst the video server and the rendering device at least partly in accordance with the first set and the second set, wherein the allocating minimizes resources allocated for upscaling at the rendering device and optimizes the video quality of the video displayed at the rendering device.