Design of 3d animation color rendering system based on image enhancement algorithm and machine learning

Abstract

Abstract At present, this paper points out the color problem of 3D animation, comprehensively studies the mechanism of machine image quality degradation and the related image enhancement methods integrating multiple scene variables, and improving the image enhancement method is an important problem to be solved in the industry. One or more image quality evaluation indicators are used to extract some features of the image, maintain the consistency with the subjective perception of the human eye, build an appropriate image quality evaluation machine model, and use machine learning technology to analyze image improvement algorithm data to achieve image quality evaluation results. With the change of lighting conditions in complex scenes, the gray distribution, clarity and color reproducibility of the captured image will have a great impact. Therefore, this paper mainly takes these three attributes as the key parameters to evaluate the image quality. At this stage, computer learning technology has developed rapidly. Therefore, based on computer learning technology, 3D animation has brought great changes. It can continuously improve the rendering quality of animation, and also increase the complexity of animation program development. The 3D animation rendering engine, including cross platform, image rendering, memory management, resource management and other related technologies, provides a simple and easy-to-use interface for developers to develop graphical programs, and greatly improves the work efficiency of developers. At the same time, some mainstream 3D color systems are analyzing the architecture and modules to design and develop a set of architecture suitable for the engine. The 3D animation color rendering system designed in this paper is mainly divided into multiple modules according to different functions. In addition, this paper uses the cache memory management strategy in the memory management module, which significantly reduces the number of dynamic memory allocation, avoids the generation of memory fragments, and improves the efficiency of memory use.