4results about How to "Save transmission bandwidth" patented technology

The method belongs to the technical field of panoramic monitoring of ultra-high voltage converter station, and aims to solve the problem that panoramic monitoring image data is directly uploaded to the cloud, occupying a large amount of cloud resources. By adopting multi-scale convolution blocks in the deep multi-scale residual network model to construct low-order and high-order features of images of various scales, the incomplete phenomenon of image detail extraction is avoided, and the residual learning mechanism is adopted to retain low-order rough features, thereby improving the reconstruction ability of the image. Topology optimization constructs the topology structure of the heterogeneous network, and the framework combining deep reinforcement learning and Monte Carlo tree search is used to construct the network according to the pre-defined topology rules. The search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network, so as to obtain more accurate prediction in the next iteration. After the data is processed in the edge side, it is transmitted to the cloud storage, saving the cloud storage space and transmission bandwidth.

This application relates to the field of network voice acquisition technology, and discloses a high-performance RTP voice acceleration acquisition method and system based on FPGA implementation. The method includes receiving raw IP packets and parsing them to obtain a set of key packet information containing five-tuple information, service type identifiers, and policy matching results; filtering out matching RTP voice packets and extracting the five-tuple information; and accessing the flow table in the first DDR4 memory through a multi-BANK interleaved parallel read / write engine to complete the query. When a flow table entry is empty, a table is created for the five-tuple flow, and a storage token corresponding to the second DDR4 storage address is requested. When a flow table entry matches, the timestamp is updated and the storage address is determined. Then, the RTP payload is extracted, written to the corresponding space, and counted. When the count reaches a threshold, an RTP voice compression frame is assembled and output. If the flow duration times out, the storage token is revoked; if the flow table entry times out, the flow table entry is revoked. This invention improves the rate of RTP voice acquisition and storage resource utilization, adapts to high-volume voice acquisition scenarios, and reduces server processing load.

The embodiment of the present application provides a kind of image format conversion method, device and video processing equipment.The method comprises the steps of: obtaining the image to be converted;Determine a plurality of pixel blocks and a plurality of encoding modes based on the image to be converted, wherein the plurality of encoding modes correspond to a plurality of mode index values;According to the luminance component deviation value of the pixel block under the plurality of encoding modes respectively, determine the target encoding mode of the pixel block, wherein the target encoding mode is one of the plurality of encoding modes;Based on the target encoding mode, a plurality of chrominance components of the pixel block are encoded to obtain a plurality of chrominance component output values of the pixel block;According to the target encoding mode, determine the target mode index value from the plurality of mode index values;Based on the target mode index value, a plurality of luminance components and the plurality of chrominance component output values, obtain target data and output.

The deep learning-based video multi-level adaptive compression cross-layer transmission optimization method and system of the application relates to the technical field of video transmission. By designing a compression level selection model based on deep learning, the optimal compression level is output based on the original video data and network environment parameters, and the corresponding compression strategy of the optimal compression level is obtained. A transmission strategy selection model is designed to generate a transmission strategy based on video quality parameters and network environment parameters. A global utility function is defined to calculate the global utility value when the compression strategy and the transmission strategy are assumed to be executed. An optimization objective function is designed, which inputs the compression strategy and the transmission strategy and outputs the optimized compression strategy and the transmission strategy. A condition judgment logic is set to select the final strategy based on the compression strategy and the transmission strategy before and after optimization, thereby achieving global optimization of video compression and transmission.