Abstract: The disclosure relates to method and system for on-device inference in a deep neural network (DNN). The method comprises: determining whether one or more layers of the DNN satisfy one of a first, a second and a third condition, the one or more layers including one or more convolution layers and one or more resampling layers; performing the on-device inference based on the determination, wherein performing the on-device inference comprises at least one of: optimizing the one or more convolution layers in the one or more parallel branches based on the one or more layers of the DNN satisfying the first condition, optimizing the at least one of the resampling layers based on the one or more layers of the DNN satisfying the second condition, and modifying operation of the at least one of the resampling layers based on the one or more layers of the DNN satisfying the third condition.

Abstract: The present disclosure refers to methods and apparatuses for processing of high-resolution video content. In an embodiment, a method includes generating a first group of video frames from the video content. The first group of video frames has a first resolution lower than a resolution of the video content and a first rate-distortion score. The method further includes generating a second group of video frames from the video content. The second group of video frames has a second resolution lower than the resolution of the video content and a second rate-distortion score. The method further includes selecting an optimal group of video frames from the first and second groups of video frames based on a comparison between the first and second rate-distortion scores. The optimal group of video frames has a rate-distortion score lower than the first and the second rate-distortion scores.