Predictive Coding for Lossless Image Compression Based on Improved PSO

• Main Authors: Tsung-Shun Lee, 李宗勳
• Other Authors: Y.-T. Juang
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/61770145319993811950

碩士 === 國立中央大學 === 電機工程學系 === 101 === In this thesis, we propose a modified optimization algorithm which is called particle swarm optimization with increasing-decreasing inertia weights (IDWPSO). Unlike the standard PSO algorithm, the proposed IDWPSO utilizes different weights for different particles. Initially, a small inertia weight is used for each particle to begin a global search. Then the individual inertia weights are respectively increasing linearly for more effective local searches. Finally, the inertia weights are switched to a larger value and then decreased quadratically to find a convergent optimum. Afterwards, the IDWPSO is applied to image coding problem as an image predictor. The IDWPSO predictor will be operated only when an edge is detected. The experimental results show that the proposed lossless image coding approach obtains more accurate image prediction. And better bit-rate compression is also obtained. As seen in the experiments, the IDWPSO is a 7% improvement over the MED (Median Edge Detector, MED), 4% over GAP (Gradient-Adjusted Prediction, GAP), and 2% over EDP (Edge-directed Prediction, EDP). These demonstrate the effectiveness of the proposed IDWPSO for the image coding.