Abstract: Nowadays,brain science is the forefront field of international scientific and technological research,while the visualization of the high-precision brain imaging data is the fundamental requirements of the structural imaging of brain neuroscience.Aiming at the problems of great quantity of data and low efficiency during rendering the brain imaging data,a compression domain visualization algorithm based on the combination of flag based classical hierarchical vector quantization and perfect spatial hashing was put forward.Firstly,the volume data is blocked,the average of each block is recorded and then the blocks are classfied according to their average gradient value.Secondly,the hierarchical vector quantization is used to compress the blocks of whose average gradient is not 0.Thirdly,the perfect spatial hashing technology based on blocking is used to store two index values obtained by compressing.Finally,the above compressed data is decompressed to obtain the recovered volume data,and then the perfect spatial hashing based on blocking is applied to compress the differential volume data obtained by making the original volume data minus the recovered volume data.When rendering,the compressed data is reloaded as textures to GPU,then decompression and visualization can be done in real time.The experiment results show that the algorithm reduces the data storage space and improves the compression ratio and can make the single machine handle larger data under the premise of ensuring the better quality of image reconstruction.

Key words: Volume visualization,Flag based classical hierarchical vector quantization,Perfect spatial hashing,Neural circuits,GPU