Mediatek Corporation

Mediatek Singapore Pte. Ltd.

Cheng Lung Jen

Chih-Wei Chen

Pei-Kuei Tsung

Yao-Sheng Wang

Chih-Wen Goo

Yu-Cheng Tseng

Kuo-Chiang Lo

Ming-En Shih

Yu-Sheng Lin

Wai Mun Wong

Tung-Chien Chen

Chun Chen Lin

Shih-Che Chen

Kuo-Chen Huang

Ping-Yuan Tsai

Hsin-Min Peng

Tsu Jui Hsu

Chia-Da Lee

Chun Hsien Wu

A system collects a training dataset for training an artificial intelligence (AI) model. The system receives high-resolution (HR) images and information of one or more regions-of-interest (ROIs) in the HR images. The system maps a stride distribution to the ROIs, and samples the HR images with non-uniform strides according to the ROIs and the stride distribution to generate corresponding low-resolution (LR) images. The system then trains the AI model to perform super-resolution (SR) operations using training pairs formed by the HR images and respective corresponding LR images.

Region-of-interest (ROI) guided sampling for AI super resolution transfer learning feature adaptation

An image processing circuit performs super-resolution (SR) operations. The image processing circuit includes memory to store multiple parameter sets of multiple artificial intelligence (AI) models. The image processing circuit further includes an image guidance module, a parameter decision module, and an SR engine. The image guidance module operates to detect a representative feature in an image sequence including a current frame and past frames within a time window. The parameter decision module operates to adjust parameters of one or more AI models based on a measurement of the representative feature. The SR engine operates to process the current frame using the one or more AI models with the adjusted parameters to thereby generate a high-resolution image for display.

Image-guided adjustment to super-resolution operations

A video processing circuit includes an input buffer, an online adaptation circuit, and an artificial intelligence (AI) super-resolution (SR) circuit. The input buffer receives input low-resolution (LR) frames and high-resolution (HR) frames from a video source over a network. The online adaptation circuit forms training pairs, and calculates an update to representative features that characterize the input LR frames using the training pairs. Each training pair formed by one of the input LR frames and one of the HR frames. The AI SR circuit receives the input LR frames from the input buffer and the representative features from the online adaptation circuit. Concurrently with calculating the update to the representative features, the AI SR circuit generates SR frames for display from the input LR frames based on the representative features. Each SR frame has a higher resolution than a corresponding one of the input LR frames.

Online AI super resolution engine and operations

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Hsinchu, Taiwan

Cheng Lung Jen