A Hyperspectral (HS) image provides observationalpowers beyond human vision capability but represents more than100 times data compared to a traditional image. To transmit andstore the huge volume of an HS image, we argue that a fundamentalshift is required from the existing 'original pixel intensity'-based coding approaches using traditional image coders (e.g.JPEG) to the 'residual' based approaches using a predictivecoder exploiting band-wise correlation for better compressionperformance. Moreover, as HS images are used in detection orclassification they need to be in original form; lossy schemes cantrim off uninteresting data along with compression, which can beimportant to specific analysis purposes. A modified lossless HScoder is required to exploit spatial-spectral redundancy usingpredictive residual coding. Every spectral band of an HS imagecan be treated like they are the individual frame of a video toimpose inter band prediction. In this paper, we propose a binarytree based lossless predictive HS coding scheme that arrangesthe residual frame into integer residual bitmap. High spatialcorrelation in HS residual frame is exploited by creating largehomogeneous blocks of adaptive size, which are then coded asa unit using context based arithmetic coding. On the standardHS data set, the proposed lossless predictive coding has achievedcompression ratio in the range of 1.92 to 7.94. In this paper, wecompare the proposed method with mainstream lossless coders(JPEG-LS and lossless HEVC). For JPEG-LS, HEVCIntra andHEVCMain, proposed technique has reduced bit-rate by 35%,40% and 6.79% respectively by exploiting spatial correlation inpredicted HS residuals.
|Title of host publication||Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications|
|Place of Publication||United States|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Number of pages||8|
|Publication status||Published - 2016|
|Event||2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Mantra on View Hotel, Surfer's Paradise, Australia|
Duration: 30 Nov 2016 → 02 Dec 2016
|Conference||2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)|
|Period||30/11/16 → 02/12/16|
Shahriyar, S., Paul, M., murshed, M., & Ali, M. (2016). Lossless Hyperspectral Image Compression Using Binary Tree Based Decomposition. In Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). IEEE, Institute of Electrical and Electronics Engineers.