Video transmission device and method

Abstract

The invention provides a video transmission device, and the device comprises a preprocessing module which is disposed at a transmitting end, and is used for decomposing an original target image in a to-be-transmitted video into a foreground factor, a background factor, and an attitude factor; the encoding system is configured at the sending end and is used for encoding the original image based on the foreground factor, the background factor and the attitude factor to obtain foreground information embedding characteristics, background information embedding characteristics and attitude information embedding characteristics and transmitting the foreground information embedding characteristics, the background information embedding characteristics and the attitude information embedding characteristics to the decoding system; and the decoding system is configured at the receiving end and is used for decoding the received embedded features to generate a reconstructed image. According to the method, image data are deconstructed into a plurality of factors at a sending end, an input image is coded into an embedded feature based on each decomposition factor, then an image is reconstructed based on the embedded feature, and a more real image is generated by using a deep convolutional generative adversarial network. On the premise that the transmission bandwidth and storage resources are reduced, the quality of the generated image is improved, and the transmission quality is ensured.

Description

technical field [0001] The present invention relates to the field of image generation, more particularly, to the technical field of feature extraction and image generation in the process of video transmission, and more particularly, to a video transmission device and method for generating high-quality images with low bandwidth occupation. Background technique [0002] In the IT3.0 era of human-machine-object ternary interconnection, tens of billions of intelligent terminals and hundreds of billions of things will be produced, which will be integrated into education, transportation, industry, agriculture, production, entertainment and many other applications. field. With the continuous improvement of people's demand for communication, video communication has gradually replaced voice as the basic carrier of communication content, and a large number of devices will communicate based on video. [0003] Compared with other methods, video communication will bring greater transmis...

AI Technical Summary

Problems solved by technology

Specifically, because the image reconstruction part of this method uses generative technology instead of reductive technology, the quality of the generated image is poor. Compared with the original image, there are problems such as low definition and blur. The reason is that the image generation technology uses a conditional generative confrontation network (CGAN), which only considers whether the image is generated or not, and does not consider the quality of the generated image

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