Abstract: Certain aspects of the present disclosure provide techniques for adaptive sampling for rendering using deep learning. This includes receiving, at a sampler in a rendering pipeline, a plurality of rendered pixel data, wherein the sampler includes a first machine learning (ML) model. It further includes generating a sampling map for the rendering pipeline using the first ML model and the plurality of rendered pixel data, including predicting a plurality of pixel values in the sampling map based on a generated distribution of pixel values. It further includes rendering an image using the sampler, the sampling map, and a denoiser in the rendering pipeline.

Abstract: Embodiments herein describe dividing a video into chunks with varying lengths based on the content within those frames. In contrast, dividing the video at a fix interval is prone to generating chunks starting at the middle of hard to encode areas, which can lead to a loss of encoder rate-control efficiency and produce visual quality gaps at the beginning of such chunks. The embodiments herein can identify a set of boundaries for dividing the video into chunks having similar lengths and with little to no impact on visual quality. In one embodiment, the boundaries of the chunks are placed at locations (or frames) that are far from the complex (or hard to encode) areas of the video. To do so, the system evaluates the video using various complexity metrics to identify the complex areas that require more bits to encode relative to less complex areas.