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In this paper, we presents a novel image progressive deliver approach based on the continuous relationship, frame prediction and frame buffer between the drawn image sequences, which is a very simple and efficient method, especially when the network is unstable or the network bandwidth is not large. It makes use of the strong continuity between the action relativity in the user's actual operation and the continuous frames in the scene rendering, selects some key frames in a series of rendered high resolution images for progressive network transmission. The client of the network can use the received series of rendered images, then reconstruct the images of other viewpoints according to the images of these key frames, and set the image frame buffer to save the generated or transmitted images, so as to ensure a better user experience when the network is unstable. In the browsing process of 3D scene, it greatly reduces the number of images that need to be drawn by the background server, and also reduces the amount of image data actually transmitted by the network. In this way, under the same network bandwidth, it can smoothly browse the super large 3D scene by multiple people in real time, and improve the user's browsing experience. First of all, our algorithm divides and recognizes the action behavior of the browsing user, transforms the continuous interaction behavior of the browsing user into a series of standard behaviors, which can quantify the interaction behavior of the user, and provides a unified standardized standard for the subsequent prediction, image generation and image caching. Then select the image to be drawn or cached according to the interactive action of the browsing user, and select the image to be sent according to certain rules. Finally, the client program reconstructs the other time according to the received important frame sequence and a series of known constraints, and uses some strategies to cache some frames to prevent network jitter.
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