Intel Corporation

Beijing University of Technology

Huan Dou

Yi-Jen Chiu

Chen Wang

Lidong Xu

Xiaoxia Cai

Ximin Zhang

Sang-hee Lee

Kebin Jia

An apparatus for super resolution imaging includes a convolutional neural network () to receive a low resolution frame () and generate a high resolution illuminance component frame. The apparatus also includes a hardware scaler () to receive the low resolution frame () and generate a second high resolution chrominance component frame. The apparatus further includes a combiner () to combine the high resolution illuminance component frame and the high resolution chrominance component frame to generate a high resolution frame ().

Super resolution using convolutional neural network

A method for image content classification is described herein. The method includes counting a number of distinct color numbers in an image. The method also includes clustering blocks with a same distinct color number into a same class and determining a block occupancy rate of each color number for the image. Finally, the method includes classifying the image according to the block occupancy rate via a plurality of classifiers communicatively coupled in series.

Image content classification

An example apparatus for enhancing video includes a decoder to decode a received 360-degree projection format video bitstream to generate a decoded 360-degree projection format video. The apparatus also includes a viewport generator to generate a viewport from the decoded 360-degree projection format video. The apparatus further includes a convolutional neural network (CNN)-based filter to remove an artifact from the viewport to generate an enhanced image. The apparatus further includes a displayer to send the enhanced image to a display.

Enhancing 360-degree video using convolutional neural network (CNN)-based filter

Techniques related to accelerated video enhancement using deep learning selectively applied based on video codec information are discussed. Such techniques include applying a deep learning video enhancement network selectively to decoded non-skip blocks that are in low quantization parameter frames, bypassing the deep learning network for decoded skip blocks in low quantization parameter frames, and applying non-deep learning video enhancement to high quantization parameter frames.

Video codec assisted real-time video enhancement using deep learning

The present disclosure relates to the technical field of video coding. Implementations herein provide methods for fast 3D video coding for high efficiency video coding HEVC. The methods speed up the view synthesis process during the rate distortion optimization for depth coding based on texture flatness. The implementations include extracting coding information from textures, analyzing luminance regularity among pixels from flat texture regions based on statistical method, judging the flat texture regions using the luminance regularity for depth maps and terminating the flat texture block's view synthesis process when processing rate distortion optimization. Compared to original pixel-by-pixel rendering methods, the implementations reduce coding time without causing significant performance loss.

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Beijing, China

Huan Dou