Abstract: A system for dynamic range expansion of digital images includes: an optimizing device configured to calculate and store the output histogram of the previous frame of image data, calculate and obtain the mapping function according to the output histogram of the previous frame of image data and obtain the mapping table of the previous frame of image data according to the mapping function; and a mapping device configured to receive a next frame of image data and process the next frame of image data with the mapping table of the previous frame of image data to generate the enhanced image data. The present system enables processing ultrahigh definition images with very low requirements on hardware requirement. There is further provided a method and a non-volatile storage medium for dynamic range expansion of digital images.

Abstract: A system for dynamic range expansion of digital images includes: an optimizing device configured to calculate and store the output histogram of the previous frame of image data, calculate and obtain the mapping function according to the output histogram of the previous frame of image data and obtain the mapping table of the previous frame of image data according to the mapping function; and a mapping device configured to receive a next frame of image data and process the next frame of image data with the mapping table of the previous frame of image data to generate the enhanced image data. The present system enables processing ultrahigh definition images with very low requirements on hardware requirement. There is further provided a method and a non-volatile storage medium for dynamic range expansion of digital images.

Abstract: Provided are an image encoding apparatus, an image decoding apparatus, an image processing system including the image encoding/decoding apparatus, a training method for the image processing system and a display apparatus.

Abstract: The present disclosure provides a method and an apparatus for stretching an image. The method for stretching an image includes: selecting a corresponding stretching mode according to a mode selection parameter; generating a corresponding stretching filter group according to a stretching parameter and the selected stretching mode, and segmenting input image data into blocks; and processing the input image data segmented into blocks by the stretching filter group, to obtain stretched image data.

Abstract: An image upscaling system includes at least two convolutional neural network modules and at least one synthesizer. The convolutional neural network module and the synthesizer are alternately connected to one another. The first convolutional neural network module receive an input image and the corresponding supplemental image, generate a first number of the feature images, and output them to the next synthesizer connected thereto. Other convolutional neural network modules each may receive the output image from the previous synthesizer and the corresponding supplemental image, generate a second number of feature images, and output them to the next synthesizer connected thereto, or output them from the image upscaling system. The synthesizer may synthesize every n*n feature images in the received feature image into one feature image and output the resultant third number of feature images to the next convolutional neural network module connected thereto or output them from the image upscaling system.

Abstract: The disclosure discloses an apparatus for upscaling an image, a method for training the same, and a method for upscaling an image, where a convolutional neutral network circuit obtains feature images of the image, a multiplexer upscales the image by integrating every n*n feature images of an input signal into a feature image with a resolution which is n times the resolution of a feature image of the image, where n is an integer greater than 1.

Abstract: An image processing method and an image processing device are provided. The image processing method includes steps of extracting a feature of an inputted first image by a first CNN, and reconstructing and outputting an image by a second CNN. The first CNN includes a plurality of first convolutional layers connected sequentially to each other and a plurality of first pooling layers each arranged between respective adjacent first convolutional layers, and each first convolutional layer is configured to generate and output a first convolutional feature. The second CNN includes a plurality of second convolutional layers connected sequentially to each other and a plurality of composite layers each arranged between respective adjacent second convolutional layers, and each composite layer is an up-sampling layer.

Abstract: The disclosure discloses an apparatus and method for correcting a flicker in a video, and a video device, the method including: determining a correction weight for correcting grayscale values of a current frame of video image according to a ratio of a variance of a histogram of mapped grayscale values of a last frame of video image to a variance of a histogram of grayscale values of the last frame of video image, and a contrast enhancement upper limit parameter input by a user, and/or a largest percentage of the number of pixels with a same grayscale among a total number of pixels in the histogram of the grayscale values of the last frame of video image, and the contrast enhancement upper limit parameter input by the user; and, determining resulting grayscale values of the current frame of video image according to the correction weight.

Abstract: A drowning alarm system and a drowning alarm method. The drowning alarm system includes a submerging detection device, a position detection device and a signal transmitting device. The submerging detection device configured to detect whether or not the submerging detection device is submerged, and generate a submerging alarm instruction when the submerging detection device is submerged. The position detection device configured to determine whether or not the submerging detection device is within a preset range, and generate a distance information instruction when the submerging detection device is within the preset range. The signal transmitting device configured to transmit a first alarm signal when the submerging detection device generates the submerging alarm instruction and the position detection device generates the distance information instruction. The drowning alarming system with false alarm prevention function is realized.

Abstract: An image upsampling system, a training method thereof and an image upsampling method are provided, the feature images of an image are obtained by using the convolutional network, upsampling processing is performed on the images with the muxer layer to synthesize every n×n feature images in the input signal into a feature image with the resolution amplified by n×n times, in the upsampling procedure with the muxer layer, information of respective feature images in the input signal is recorded in the generated feature image(s) without loss; and thus, every time when the image passes through a muxer layer with an upsampling multiple of n, the image resolution can be increased by n×n times.

Abstract: An image upsampling system, a training method thereof and an image upsampling method are provided, the feature images of an image are obtained by using the convolutional network, upsampling processing is performed on the images with the muxer layer to synthesize every n×n feature images in the input signal into a feature image with the resolution amplified by n×n times, in the upsampling procedure with the muxer layer, information of respective feature images in the input signal is recorded in the generated feature image(s) without loss; and thus, every time when the image passes through a muxer layer with an upsampling multiple of n, the image resolution can be increased by n×n times.

Abstract: The present disclosure provides a method and device for correcting video flicker.

Abstract: The disclosure relates to an image compression system, an image decompression system, a training method and device, as well as a display device. In the image compression system, convolutional neural network modules are used to complete the update and prediction processes. As such, the weight of each filtering unit in the convolutional neural network module can be trained in order to provide the corresponding image compression system with a better compression rate, thereby reducing the difficulty in setting the filtering parameters of the image compression unit and the image decompression unit.

Abstract: The disclosure relates to an image compression system, an image decompression system, a training method and device, as well as a display device. In the image compression system, convolutional neural network modules are used to complete the update and prediction processes. As such, the weight of each filtering unit in the convolutional neural network module can be trained in order to provide the corresponding image compression system with a better compression rate, thereby reducing the difficulty in setting the filtering parameters of the image compression unit and the image decompression unit.

Abstract: A super-resolution method, a super-resolution system, a user equipment and a server. The super-resolution method includes: training an image sample at a server; obtaining a server-side image database; updating a local image database in a user device by using the server-side image database; and displaying an input low-resolution image as a high-resolution image.

Abstract: According to embodiments of the present disclosure, a method for processing an adaptive media service at an encoder includes a first acquisition step of acquiring a first data stream including first image encoding data obtained by encoding a first image sequence, a second acquisition step of acquiring at least one second data stream, each second data steam including second image encoding data obtained by encoding a second image sequence and a target optimization parameter corresponding to the second image encoding data, a first selection step of selecting one data stream from a first data stream set in accordance with a condition of the receiver, the first data stream set at least including the first data stream and the at least one second data stream, and a first transmission step of transmitting the selected data stream to the receiver.

Abstract: The present disclosure discloses a method and device for image encoding and a method and device for image decoding. The present disclosure relates to the field of image processing and may solve a technical issue of inflexible image encoding. The particular technical solution comprises: acquiring a first image; compressing the first image to generate a second image, a resolution of the second image is lower than that of the first image; encoding the second image to generate a first bit stream; determining a parameter corresponding to the first image from a parameter database, the parameter corresponds to detail information of the first image; generating a second bit stream according to the parameter; and combining the first bit stream and the second bit stream into a transmission bit stream. The present disclosure may be used for image encoding.

Abstract: There is provided an image interpolation method and an image interpolation apparatus. The image interpolation method comprising: interpolating pixels of a source image with zeros to form an up-sampling image; obtaining a reference interpolation kernel using the up-sampling image; and convolving the pixels of the source image, the reference interpolation kernel and a directional shift coefficient matrix to perform reference kernel interpolation based on directional shift on the source image.