This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.

We propose an improved HVQ approach by combining two detail levels to compress space environment volumes, based on the variation characteristics including smooth variation and significantly positive correlation between variations in the same direction at two different scales. First, the space environment data is divided into 43 blocks. Then blocks are decomposed into a two-level hierarchical representation and each block is represented by a mean value and a detail vector. Finally, the detail vectors are encoded by a vector quantizer. During the vector quantization process, PCA-split is applied to compute an initial codebook, and then LBG-algorithm is conducted for codebook refinement and quantization. We take advantage of the codebook-retraining method to speed up the quantization of time series with temporal coherence. Furthermore, we employ the progressive rendering based on GPU for realtime interactive visualization. The results of our experiments prove that the algorithm proposed in this paper can significantly improve the compression rate and decoding speed without sacrificing fidelity in space environment domain.

No abstract