A novel compression algorithm for infrared thermal image sequence based on K-means method

High resolution in space and time is becoming the new trend of thermographic inspection of equipments, therefore, the development of a fast and precise processing and data store technique of high resolution thermal image should be well studied. This article will propose a novel global compression algorithm, which will provide an effective way to improve the precision and processing speed of thermal image data. This new algorithm is based on the decay of the temperature of thermograph and the feature of thermal image morphology. Firstly, it will sort the data in space according to K-means method. Then it will employ classic fitting calculation to fit all the typical temperature decay curves. At last, it will use the fitting parameters of the curves as the parameters for compression and reconstruction of thermal image sequence to achieve the method for which the thermal image sequence can be compressed in space and time simultaneously. To validate the proposed new algorithm, the authors used two embedded defective specimens made of different materials to do the experiment. The results show that the proposed infrared thermal image sequence compression processing algorithm is an effective solution with high speed and high precision. Compared to the conventional method, the global compression algorithm is not only noise resistant but also can improve the computing speed in hundreds of times.