Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media for upscaling video. An apparatus configured to process one or more frames (e.g., one or more video frames) comprises one or more processors coupled to the one or more memories. The one or more processors are configured to: obtain a first frame from an image sensor; downsample the first frame into a first downsampled frame based on a transfer function associated with the image sensor; and encode the first downsampled frame.

Abstract: Techniques and systems are provided for image demosaicing. For instance a process can include performing a depth-wise convolution operation on image data and a depth-wise convolutional filter with pre-determined parameter values to obtain a plurality of color channels for the image data; performing a convolution operation on the plurality of color channels to obtain a processed plurality of color channels; arranging the processed plurality of color channels into a demosaiced image; and outputting the demosaiced image.

Abstract: A computer-implemented method for on-device video query and search using an artificial neural network (ANN) includes receiving by the ANN, a video and a search query. The video includes a sequence of frames and associated subtitle information. First representations for a first set of words in the search query and second representations for a second set of words in the subtitle information are generated, at the mobile device by the ANN. A correlation between the search query and the subtitle information is determined at the mobile device by the ANN based on the first representations and the second representations. The ANN, at the mobile device. predicts a portion of the video including content responsive to the search query based on the correlation.

Abstract: Systems and techniques are provided for processing image data. For instance, a process can include classifying a first image as a key frame, based on a difference between the first image and a previous image, wherein the first image and the previous image are included in a plurality of images. A first machine learning network can generate an enhanced key frame image corresponding to the first image and a hidden state output associated with the enhanced key frame image. A second image of the plurality of images can be classified as a dependent frame, based on a similarity between the second image and the first image. A second machine learning network can generate an enhanced dependent frame image corresponding to the second image, wherein the enhanced dependent frame image is based on the second image and the hidden state output associated with the enhanced key frame image.