Research Of Preprocessing Data Based On Compression Domain Volume Rendering

The brain neural circuits are the material basis of the brain’s exercising all the functions.Deeply understanding and accurately acknowledging the structure and function of neural circuits of the brain through efficiently processing and visually analyzing the large-scale high-precision sample data of neural circuits,and cracking the mystery of the brain,is a major demand of social and technological development of our country.Sample the brain structure of a mouse with the most advanced high-precision optical brain imaging equipment at home and abroad,you can get the whole brain imaging data of hundreds GB to several TB.In the view of the huge data size,it is no possible to render directly the volume data.Thus,prior to visualization,need to compress and process raw data,to reduce the amount of data,it is an effective technical way for improving the visualization efficiency of the amount data of brain circuits to study the volume visualization based on compression domain.The main work and research results of this paper can be concluded as follows:1.Design and implement a data storage structure with multiresolution branch-on-need octrees which organizes and manages the amounts of high-precision optical brain imaging data.First,block the volume data into resolution blocks and obtain the data blocks like this,then compute the actual range of each leaf node according to the actual range division rule of BONOs.When constructing parallelly,according to the corresponding rule of the octal coding and octree,each computing node reads the corresponding image data to generate the leaf node data according to their computing tasks.Then the leaf nodes staute to higher level internal nodes,following by cycle to obtain the multiresolution branch-on-need octrees.Finally,compress the each node data using the compression algorithm based on the combination of flag based classical hierarchical vector quantization and perfect spatial hashing from this paper.2.Put forward a compression domain visualization algorithm based on the combination of flag based classical hierarchical vector quantization and perfect spatial hashing.Firstly,block the volume data,record the average of each block and then classify the blocks whether their average gradient values are 0 or not.Secondly,using the hierarchical vector quantization,compress the blocks of which average gradient is not 0.Thirdly,use the perfect spatial hashing technology based on blocking to store two index values obtained by compressing.Finally,decompress the above compressed data to obtain the recovered volume data,and then apply the perfect spatial hashing based on blocking to compress the differential volume data obtained by making the original volume data minus the recovered volume data.When rendering,reload the compressed data as textures to GPU,then decompression and visualization can be done in real time.We make a good visualization of experimental results.3.The system of high-speed high-quality 3D visualization software prototype system of brain circuits is designed.The system consists of data preprocessing module,rendering module and interactive module.The system maintains two caches in memory which correspond to two blocks,loads a block which is selected by user into a cache and constructs the textures,and then reloads them into the GPU.Then decompress and render the compressed data in GPU using the volume visualization methods provided by the rendering module.The interactive module provides the image interacting functions,including pattern rotation,translation and scaling,eliminates the bleak regions and focuses on the interesting regions.We do visual experiments for the amounts of brain neural circuits in mice with this prototype system,verifying the correctness and effectiveness of the proposed method.