Animation Vr Scene Stitching Modeling Based on Genetic Algorithm

Abstract

The animation scene stitching modeling is further investigated in conjunction with Genetic Algorithms (GA) in order to advance the technology based on the currently used animation scene stitching modeling. To retrieve the low-frequency and high-frequency coefficients of the animation scene, the technique employs wavelet transform. Using a comparison and screening of the convolution results from two GA template operators, a set of splicing criteria is chosen for the high-frequency coefficients; The Laplace sharpness assessment function and the 8-neighborhood local variance are used to determine the splicing rule for the low-frequency coefficients; In order to produce the mosaic modeling of the diffuse scene, the inverse wavelet transform is utilized. The subjective and objective assessment approaches are used together to examine the experimental outcomes. The data demonstrates that the GA achieves a higher quality splice than the traditional splicing modeling. Rich edge information and great scene clarity are benefits of animation scene splicing modeling.