Deep learning-based image compression method applied to three-dimensional printing of furniture

Abstract

The invention discloses a deep learning-based image compression method applied to furniture three-dimensional printing. The method comprises the steps of collecting furniture surface texture image data; carrying out quantitative coding on the furniture surface texture image data; constructing an image entropy neural network model; inputting the quantized and encoded image data into an image entropy neural network model for training to obtain an image compression network model; inputting to-be-printed furniture surface texture image data into the image compression network model to obtain compressed furniture surface texture image data; and transmitting the compressed image data to a three-bit printer to perform decompression and printing processes. The neural network model can effectively compress the furniture texture image with rich structure information and unique geometric textures, and the mean square error between the original image and the decompressed image is reduced to the maximum extent; when the method is applied to three-dimensional printing of furniture, the occupied space of the memory can be effectively reduced, and the resolution of a printed image is improved.

Description

technical field [0001] The invention belongs to the field of computer image processing, in particular to an image compression method based on deep learning applied to three-dimensional printing of furniture. Background technique [0002] With the continuous improvement of 3D printing technology and the rapid development of machine learning-based digital image processing technology, the 3D printing technology that uses new bonding materials based on digital models and constructs objects by layer-by-layer printing has been gradually applied to All walks of life. In the technological process of modern furniture production in my country, 3D printing has been gradually applied to the innovation of furniture modeling design and innovation of furniture materials. However, due to the limitations of the 3D printing process itself, and the complex color texture and geometric texture of the furniture texture image, the requirements for image refinement are high, and the resolution is ...

AI Technical Summary

Problems solved by technology

The image compression processing methods in the prior art lack encoding methods for three-dimensional or even multi-dimensional images, and traditional compression methods cannot meet the performance requirements of furniture texture pattern compression; they lack the use of context models as rate-distortion items to simultaneously train two encoders to improve depth image compression performance network of

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