使用快速分裂方法應用於影像壓縮的技術

Image Compression Technique Using Fast Divisive Scheme

莫清原(Ching-Yuan Mo)

指導教授 ： 蔡正發

國立屏東科技大學/管理學院/資訊管理系所/碩士(2011年)

https://doi.org/10.6346/NPUST.2011.00151

摘要

在影像壓縮領域中，LBG是一個速度快，結構簡單易懂的方法，而訓練後之壓縮品質亦可，在向量量化(Vector Quantization, VQ)領域中佔有重要一席之地。另一種分割式的演算法細胞分裂演算法，結構更為簡單，省去LBG的檢查收歛情形，以一分裂為二的方式加減向量值，速度更快，但是PSNR值卻不盡理想。
　　本論文提出一個基於細胞分裂法的理念上更快速的分裂向量的演算方法，同時要避免因為缺代優化訓練而造成壓縮品質不夠理想的遺憾，所以也參酌了LBG的方法應用於其中，期使PSNR值提高於水準之上，欲使造就出一個結構簡單、訓練品質優良、壓縮時間快速的演算法。

關鍵字

LBG ； 向量量化 ； 細胞分裂演算法 ； 影像壓縮

並列摘要

In the field regarding image compression, as LBG is a fast and easily understanding method with simple construction, and its compressed quality after training is acceptable, LBG is always considered an important technique in VQ(Vector Quantization)field. The other divisive algorithm is Cell Divisive Algorithm. Its construction is much simpler as it removes the precedure of checking convergent status in LBG, and add or subtract vector's value after dividing one into two. The whole procedure is faster, but the PSNR value is not satisfactory.
The thesis is to propose a faster divisive vector algorithm using cell divisive algorithm. Since the defect of disqualified compression quality should be avoided due to lack of optimized training, LBG method is also applied in the algorithm to improve PSNR value. The purpose of the algorithm is to set up a easily-constructed, quickly-compressed algorithm with well-trained quality.

並列關鍵字

LBG ； vector quantization ； divisive neuron algorithm ； image compression

參考文獻

[5]林于峻，具新的高效能與高效率之增強型自組織特徵映射圖於影像壓縮問題之研發，國立屏東科技大學資訊管理學系碩士論文，2009。

[6]Gray, R.M., "Vector Quantization," IEEE ASSP Magazine, Vol. No. 2, pp. 4-29, 1984.

[7]Kohonen, T., "Self-organizing Maps," Springer-Verlag, Berlin, 1995.

[8]Kohonen, T., "The Self-Organizing Maps," In:Proceedings of the IEEE, Vol. 78, No. 9, 1464-1480, 1990.

[9] Madeiro, F., Vilar, R. and Neto, B., “A Self-Organizing Algorithm for Image Compression,” IEEE Trans. on Neural Networks, Vol. 28, pp. 146-150, 1998.