Embodiments of the present invention provide a video communication method and system, and a terminal, to improve a success rate of video communication by intelligently obtaining a video encoding/decoding format used for video communication. Specifically, the method includes: sending, by a sending terminal, an invite message to a receiving terminal; receiving, by the sending terminal, a reply message; determining that a video encoding/decoding format supported by the sending terminal does not include a video encoding/decoding format included in the reply message; obtaining, by the sending terminal, a video encoding/decoding format supported by both the sending terminal and the receiving terminal; and performing, by the sending terminal, video communication with the receiving terminal by using the obtained video encoding/decoding format.

To enable a good HDR image or video coding technology, being able to yield high dynamic range images as well as low dynamic range images, we invented a method of encoding a high dynamic range image (M_HDR), comprising the steps of:

converting the high dynamic range image to an image of lower luminance dynamic range (LDR_o) by applying a) scaling the high dynamic range image to a predetermined scale of the luma axis such as [0,1], b) applying a sensitivity tone mapping which changes the brightnesses of pixel colors falling within at least a subrange comprising the darker colors in the high dynamic range image, c) applying a gamma function, and d) applying an arbitrary monotonically increasing function mapping the lumas resulting from performing the steps b and c to output lumas of the lower dynamic range image (LDR_o); and

outputting in an image signal (S_im) a codification of the pixel colors of the lower luminance dynamic range image (LDR_o), and

outputting in the image signal (S_im) values encoding the functional behavior of the above color conversions as metadata, or values for the inverse functions, which metadata allows to reconstruct a high dynamic range image (Rec_HDR) from the lower luminance dynamic range image (LDR_o).

An image quality enhancing apparatus which make a learning-type image quality enhancing method utilizing a sparse expression practical are provided. The apparatus calculates, from the feature quantity of an image, coefficients of low-image-quality base vectors expressing a feature quantity with a linear sum and generates the image with the image quality enhanced by calculating a linear sum of high-image-quality base vectors using the calculated coefficient. When calculating the coefficient, the number of base vectors with non-zero coefficients is determined, the determined number of base vectors is selected, and a solution of a coefficient matrix is calculated by assuming the coefficients of the base vectors other than the selected base vectors are zero. The amount of processes necessary for obtaining a sparse solution of a coefficient matrix can be reduced by adjusting the number of base vectors with non-zero coefficients, and a practical image quality enhancing apparatus can be realized.

Embodiments described herein provide various examples of monitoring adverse events in the background while displaying a higher-resolution surgical video on a lower-resolution display device. In one aspect, a process for detecting adverse events during a surgical procedure can begin by receiving a surgical video. The process then displays a first portion of the video images of the surgical video on a screen to assist a surgeon performing the surgical procedure. While displaying the first portion of the video images, the process uses a set of deep-learning models to monitor a second portion of the video images not being displayed on the screen, wherein each deep-learning model is constructed to detect a given adverse event among a set of adverse events. In response to detecting an adverse event in the second portion of the video images, the process notifies the surgeon of the detected adverse event to prompt an appropriate action.

Embodiments of the disclosure include a method, a device, and a computer program product for video processing. This method includes: selecting frames having features of a first type from a first instance of a video as a first candidate set, the first instance having a first resolution; generating a set of training frames based at least on the first candidate set; acquiring a set of corresponding frames for the set of training frames in a second instance of the video, the second instance having a second resolution lower than the first resolution; and determining, using the set of training frames and the set of corresponding frames, a conversion parameter for conversion from the second resolution to a third resolution. This solution provides a smaller-scale and higher-quality training set for the training of a video conversion model, thus improving the quality of training while saving computational resources and increasing training speed.

An information processing apparatus includes a control unit that creates a plurality of learning information items including an input image and a teacher image as an expected value by image-processing the input image in accordance with a scenario described with a program code, and supplies the created plurality of learning information items to a machine learning module that composes an image processing algorithm by machine learning.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method comprising receiving at least a plurality of neighbouring sections of lower-quality visual data. A plurality of input sections from the received plurality of neighbouring sections of lower quality visual data are selected and features are extracted from those plurality of input sections of lower-quality visual data. A target section based on the extracted features from the plurality of input sections of lower-quality visual data is then enhanced.

The utility model provides system and apparatus for communication between different interfaces. The system includes at least an image process module for processing at least an image signal produced from a least an industrial embedded system, a remote control module coupled to at least said image process module for receiving said processed image signal and transmitting it to a remote computer, wherein said remote control module further receiving a control command of said remote computer, and at least a control module coupled to said remote module for receiving and processing said command.

A super-resolution coding mode is described. An encoded image can be decoded from an encoded bitstream stored on a non-transitory computer-readable storage medium. A flag can indicate whether an image was encoded using the super-resolution mode at a first resolution. Responsive to the flag indicating that the image was encoded using the super-resolution mode, bits indicating an amount of scaling of the image are included. The image is decoded from the encoded bitstream to obtain a reconstructed image at the first resolution, and the reconstructed image is upscaled to a second resolution using the amount of scaling to obtain an upscaled reconstructed image. The second resolution is higher than the first resolution. Loop restoration parameters within the bitstream can used for look restoration filtering of the upscaled reconstructed image to obtain a loop restored image at the second resolution.