A blue noise pattern sampling method based on cloud computing to prevent aliasing

Abstract

The high frequency of the image through pre-filtering and sampling cannot be eliminated, whereby the power spectrum of the oscillation may appear the aliasing phenomenon, the sampling scheme based on cloud computing proposed two standard blue noise patterns: step blue noise and unimodal blue noise. However, a large number of sampling points usually results in large processing requirements. In this paper we propose an object-order algorithm by using an octree and n-bit quantised gray, MIP average complexity can be reduced to O (nˆ2). This improvement makes the interactive visualisation and the data storage security of MIP greatly improved in large capacity data application. Experimental results show that the low sampling rate model based on cloud computing can effectively prevent aliasing structure, in a high sampling rate model based on cloud computing also perform equally well. Simulation results employing H.264's redundant slice mechanism show significant performance gains over conventional error-resilient encoding methods and native redundant encoding methods.