Mediatek Corporation

Chih-Wen Goo

Pei-Kuei Tsung

Chih-Wei Chen

Yao-Sheng Wang

Yu-Sheng Lin

Yu-Cheng Tseng

Cheng-Lung Jen

Kuo-Chiang Lo

Shih-Chin Lin

Ying-Chieh Chen

Tung-Chien Chen

Shih-Che Chen

Cheng Lung Jen

Ming-En Shih

Ping-Yuan Tsai

Chien-Yu Huang

Shih-Wei Hsieh

Chia-Da Lee

Kuo-Chen Huang

Po-Hua Huang

Shu-Hsin Chang

Tsung-Shian Huang

Yu-Lin Hou

Jen Cheng Lung

Huang Tsung-Shian

Chun Hsien Wu

Hsin-Min Peng

Tsu Jui Hsu

Chia-Ni Lu

You Yu Nian

Mingen Shih

A system receives a neural network model that includes asymmetric operations. Each asymmetric operation includes one or more fixed-point operands that are asymmetrically-quantized from corresponding floating-point operands. The system compiles a given asymmetric operation of the neural network model into a symmetric operation that includes a combined bias value. A compiler computes the combined bias value is a constant by merging at least zero points of input and output of the given asymmetric operation. The system then generates a symmetric neural network model including the symmetric operation for inference hardware to execute in fixed-point arithmetic.

Compiling asymmetrically-quantized neural network models for deep learning acceleration

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 frame interpolation method for generating a third image frame interpolated between a first image frame and a second image frame includes: performing motion estimation upon a first input image frame and a second input image frame, to obtain a single-directional motion, wherein the first input image frame is derived from the first image frame, and the second input image frame is derived from the second image frame; scaling the single-directional motion according to a time point of the third image frame, to generate a scaled motion; deriving a forward-warped result from a result of performing a forward warping operation and a first inverse operation upon the scaled motion; performing a second inverse operation upon the forward-warped result, to generate an inversed result; and generating the third image frame according to the first image frame, the second image frame, the forward-warped result, and the inversed result.

Method for performing frame interpolation based on single-directional motion and associated non-transitory machine-readable medium

Aspects of the disclosure provide a frame processor for processing frames with aliasing artifacts. For example, the frame processor can include a super-resolution (SR) and anti-aliasing (AA) engine and an attention reference frame generator coupled to the SR and AA engine. The SR and AA engine can be configured to enhance resolution and remove aliasing artifacts of a frame to generate a first high-resolution frame with aliasing artifacts and a second high-resolution frame with aliasing artifacts removed. The attention reference frame generator can be configured to generate an attention reference frame based on the first high-resolution frame and the second high-resolution frame.

AI frame engine for mobile edge

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

Aspects of the disclosure provide a device for processing frames with aliasing artifacts. For example, the device can include a motion estimation circuit, a warping circuit coupled to the motion estimation circuit, and a temporal decision circuit coupled to the warping circuit. The motion estimation circuit can estimate a motion value between a current frame and a previous frame. The warping circuit can warp the previous frame based on the motion value such that the warped previous frame is aligned with the current frame and determine whether the current frame and the warped previous frame are consistent. The temporal decision circuit can generate an output frame, the output frame including either the current frame and the warped previous frame when the current frame and the warped previous frame are consistent, or the current frame when the current frame and the warped previous frame are not consistent.

A video processing system includes an input port and a video processing circuit. The input port obtains device information of a display panel. The video processing circuit obtains an input frame and the device information, configures an image enhancement operation according to the device information, generates an output frame by performing the image enhancement operation upon the input frame, and transmits the output frame to the display panel for video playback.

Video processing system for performing artificial intelligence assisted picture quality enhancement and associated video processing method

Growing community of inventors

Hsin-Chu, Taiwan

Chih-Wen Goo