MACHINE-LEARNING-BASED ADAPTATION OF CODING PARAMETERS FOR VIDEO ENCODING USING MOTION AND OBJECT DETECTION

摘要

The present disclosure relates to encoding of a video image using coding parameters, adapted on basis of motion of the video image and of an output of a machine-learning based model, which is fed with samples of a block of the video image and motion information of the samples. With this input along with texture, the machine-learning model segments the video image into regions based on the strength of motion determined from the motion information. An object is detected within the video based on motion and texture, and the spatial-time coding parameters are determined based on strength of the motion, and whether or not the detected objects moves. The use of the machine-learning model, fed with motion information and block samples, combined with texture information of the object allows for a more accurate image segmentation, and thus optimization of coding parameters depending on the importance of the image content in terms of less relevant background and dynamic image content, including fast and slow moving objects of different sizes.