Abstract: Aspects of the present disclosure provide an automated labeling system. For example, the automated labeling system can include an automated labeling module (ALM) configured to receive wireless signals and ground truth of learning object and label the wireless signals with the ground truth when receiving the ground truth to generate labeled training data. The automated labeling system can also include a training database coupled to the ALM. The training database can be configured to store the labeled training data.

Abstract: A method of feature extraction from an image can include receiving the image including pixels, generating confidence values corresponding to positions of the pixels in the image by an artificial intelligence (AI) based feature extractor, selecting a first position among the positions of the pixels in the image, a first confidence value among the generated confidence values at the first position being higher than a first threshold, and generating a final set of keypoint-descriptor pairs based on the confidence values corresponding to positions of the pixels in the image. The final set of keypoint-descriptor pairs includes at least two keypoint-descriptor pairs corresponding to the first position among the positions of the pixels in the image.

Abstract: 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.

Abstract: 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.

Abstract: An analog circuit is calibrated to perform neural network computing. Calibration input is provided to a pre-trained neural network that includes at least a given layer having pre-trained weights stored in the analog circuit. The analog circuit performs tensor operations of the given layer using the pre-trained weights. Statistics of calibration output from the analog circuit is calculated. Normalization operations to be performed during neural network inference are determined. The normalization operations incorporate the statistics of the calibration output and are performed at a normalization layer that follows the given layer. A configuration of the normalization operations is written into memory while the pre-trained weights stay unchanged.

Abstract: An image processing circuit stores a training database and models in memory. The image processing circuit includes an attribute identification engine to identify an attribute from an input image according to a model stored in the memory. By enhancing the input image based on the identified attribute, a picture quality (PQ) engine in the image processing circuit generates an output image for display. The image processing circuit further includes a data collection module to generate a labeled image based on the input image labeled with the identified attribute, and to add the labeled image to the training database. A training engine in the image processing circuit then re-trains the model using the training database.

Abstract: A method of feature extraction from an image can include receiving the image including pixels, generating confidence values corresponding to positions of the pixels in the image by an artificial intelligence (AI) based feature extractor, selecting a first position among the positions of the pixels in the image, a first confidence value among the generated confidence values at the first position being higher than a first threshold, and generating a final set of keypoint-descriptor pairs based on the confidence values corresponding to positions of the pixels in the image. The final set of keypoint-descriptor pairs includes at least two keypoint-descriptor pairs corresponding to the first position among the positions of the pixels in the image.

Abstract: 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.

Abstract: Techniques and implementations related to automatic image capture during preview, as well as recommendation of images, are described. A method for operating an electronic apparatus may involve receiving a stream of one or more preview images in a preview mode of the electronic apparatus. The method may also involve determining whether any preview image of the stream of preview images is valuable. The method may further involve controlling an operation of the electronic apparatus in response to the determining.

Abstract: Techniques and implementations related to automatic image capture during preview, as well as recommendation of images, are described. A method for operating an electronic apparatus may involve receiving a stream of one or more preview images in a preview mode of the electronic apparatus. The method may also involve determining whether any preview image of the stream of preview images is valuable. The method may further involve controlling an operation of the electronic apparatus in response to the determining.

Abstract: The invention provides a video processing apparatus. In one embodiment, the video processing apparatus includes a decoder, a detector, and a motion estimation and motion compensation (MEMC) module. The decoder decodes video data to generate a series of video frames with time stamps. The detector detects discontinuity of the video frames to generate discontinuity information. The MEMC module selects a previous frame prior to the discontinuity and a subsequent frame after the discontinuity from the video frames according to the discontinuity information, performs a motion estimation process to determine at least one motion, performs a motion compensation process according to the motion vector to synthesize an interpolated frame from the previous frame and the subsequent frame, and inserts the interpolated frame into the video frames to obtain a series of compensated frames.