A New Lossless Compression Algorithm for Vector Maps

2008 ◽  
Author(s):  
Ling Yun ◽  
Qian Chen-yi ◽  
Wang Xun
Keyword(s):  
Lossless Compression ◽  
Compression Algorithm ◽  
Vector Maps

An Efficient Lossless Compression Algorithm for Vector Maps

2010 ◽  
Vol 439-440 ◽  
pp. 215-219
Author(s):  
Hai Yan Wu
Keyword(s):  
Lossless Compression ◽  
Original Data ◽  
Compression Algorithm ◽  
Compression Effect ◽  
Vector Map ◽  
Graphic Data ◽  
Vector Maps ◽  
Coding Method ◽  
Prediction Coding

This paper suggested a new efficient lossless compression algorithm for vector maps in view of the graphical characteristics. First, we adopt the differential lossless transformation for the graphic data in the vector map files, to make the original data which should have been represented by double model or float model be represented by long model. Then, based on the abbreviated compression, the prediction coding based on the irregular coefficients is used. Namely, it is to dynamically distribute proper byte storage to the transformed coefficients which have been differentiated according to the numerical values, and compressed further combined with a dictionary coding method. Finally, we do an experimental contrast for the compression effect.

An Image Lossless Compression Algorithm Based on 3-PVLC

2014 ◽  
Vol 39 (8) ◽  
pp. 1289-1294
Author(s):  
Jian GAO ◽  
Jun RAO ◽  
Rui-Peng SUN
Keyword(s):  
Lossless Compression ◽  
Compression Algorithm

Study on Nearly Lossless Compression with Progressive Decoding

2014 ◽  
Vol 926-930 ◽  
pp. 1751-1754
Author(s):  
Hong Mei Song ◽  
Hai Wei Mu ◽  
Dong Yan Zhao
Keyword(s):  
Compression Ratio ◽  
Image Data ◽  
Lossless Compression ◽  
Image Transmission ◽  
Standard Test ◽  
Compression Algorithm ◽  
Progressive Transmission ◽  
Image Information ◽  
Dct Transform ◽  
Progressive Image Transmission

A progressive transmission and decoding nearly lossless compression algorithm is proposed. The image data are grouped according to different frequencies based on DCT transform, then it uses the JPEG-LS core algorithmtexture prediction and Golomb coding on each group of data, in order to achieve progressive image transmission and decoding. Experimentation on the standard test images with this algorithm and comparing with JPEG-LS shows that the compression ratio of this algorithm is very similar to the compression ratio of JPEG-LS, and this algorithm loses a little image information but it has the ability of the progressive transmission and decoding.

Czip: A Fast Lossless Compression Algorithm for Climate Data

2016 ◽  
Vol 44 (6) ◽  
pp. 1248-1267 ◽  
Author(s):  
Xiaomeng Huang ◽  
Yufang Ni ◽  
Dexun Chen ◽  
Songbin Liu ◽  
Haohuan Fu ◽  
...  
Keyword(s):  
Lossless Compression ◽  
Compression Algorithm ◽  
Climate Data
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