Mediatek Corporation

Altek Semiconductor Corp.

Yao-Sheng Wang

Pei-Kuei Tsung

Yu-Sheng Lin

Chih-Wei Chen

Chih-Wen Goo

Hong-Long Chou

Shih-Chin Lin

Ying-Chieh Chen

Wai Mun Wong

Yu-Chih Wang

Cheng-Lung Jen

Chun Chen Lin

Kuo-Chiang Lo

Cheng Lung Jen

Chao-Min Chang

Ming-En Shih

Chia-Ching Lin

Chiani Lu

Chien-Hung Lin

Po-Yu Chen

Wen-Yan Chang

Che-Lun Chuang

Chih-wei Chen

Yu-Cheng Tseng

Shih-Che Chen

Chien-Yu Huang

Chiung-Fu Chen

Chih-cheng Chen

Tung-Chien Chen

Chia-Da Lee

Kuo-Chen Huang

Tsung-Shian Huang

Ping-Yuan Tsai

Chia-Ni Lu

Hsiao-Chien Chiu

Tsu Jui Hsu

Hsin-Min Peng

Chun Hsien Wu

Huang Tsung-Shian

Jen Cheng Lung

Yu Chieh Lan

A booster engine enhances the quality of a frame sequence. The booster engine receives, from a first stage circuit, the frame sequence with quality degradation in at least a frame. The quality degradation includes at least one of uneven resolution and uneven frame per second (FPS). The booster engine queries an information repository for reference information on the frame, using a query input based on at least a region of the frame to obtain a query output. The booster engine then applies a neural network to the query input and the query output to generate an optimized frame, and sends an enhanced frame sequence including the optimized frame to a second stage circuit.

Frame sequence quality booster using information in an information repository

The quality of a frame sequence is enhanced by a booster engine collaborating with a first stage circuit. The first stage circuit adjusts the quality degradation of the frame sequence when a condition in constrained resources is detected. The quality degradation includes at least one of uneven resolution and uneven frame per second (FPS). The booster engine receives the frame sequence from the first stage circuit, and generates an enhanced frame sequence based on the frame sequence for transmission to a second stage circuit.

Frame sequence quality booster under uneven quality conditions

A device produces a dolly zoom effect with automatic focal length adjustment. The device uses a camera to capture an initial image including at least a foreground object and a background. The device includes a size tracking circuit to identify the size of the foreground object in the initial image. The device further includes a focal length control circuit. The focal length control circuit calculates an adjusted focal length of the camera to maintain the size of the foreground object in subsequently captured images.

Automatic dolly zoom image processing device

A system produces a dolly zoom effect by utilizing side view information. The system first captures a main image at a main location. The main image includes at least a foreground object of a given size and a background. The system calculates one or more side view locations based on a zoom-in factor to be applied to the background and an estimated size of the foreground object. The system then guides a user to capture one or more side view images at the one or more side view locations. The foreground object of the given size is superimposed onto a zoomed-in background. Then the side view information is used by the system to perform image inpainting.

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 system performs convolution operations based on an analysis of the input size. The input includes data elements and filter weights. The system includes multiple processing elements. Each processing element includes multipliers and adders, with more of the adders than the multipliers. According to at least the analysis result which indicates whether the input size matches a predetermined size, the system is operative to select a first mode or a second mode. In the first mode, a greater number of the adders than the multipliers are enabled for each processing element to multiply transformed input and to perform an inverse transformation. In the second mode, an equal number of the adders and the multipliers are enabled for each processing element to multiply-and-accumulate the input. One or more of the multipliers are shared by the first mode and the second mode.

Hybrid non-uniform convolution transform engine for deep learning applications

An image capturing device and a method for detecting image deformation thereof are provided. The method is for the image capturing device having a first sensor and a second image sensor, and the method includes the following steps. A first image is captured through the first image sensor, and a second image is captured through the second image sensor. A deform detection is performed according to the first and second images so as to obtain a comparison information between the first and second images. Whether a coordinate parameter relationship between the first and second images being varied is determined according to the comparison information, in which the coordinate parameter relationship is associated with a spatial configuration relationship between the first and second image sensors.

Image capturing device and method for detecting image deformation thereof

The present disclosure illustrates a lens distortion correction method to solve influence for distortion correction caused by an alignment error between a lens center and a center of image sensor unit during assembling. The present disclosure is characterized in that the spatial geometric calibration is incorporated with the lens distortion correction and different image centers are selected repeatedly when transformation relationship of image coordinates is used to perform the lens distortion correction, so as to correct the image center of the image to be corrected and enhance the accuracy of lens distortion correction.

Lens distortion correction method

Growing community of inventors

Tainan, Taiwan

Yao-Sheng Wang