LowComplexity Adaptive Switched PredictionBased Lossless Compression of TimeLapse Hyperspectral Image Data Timelapse hyperspectral image (HSI) data has an enormous size and demands lossless compression for most of the high fidelity applications. In literature, spatial and spectral correlations in HSI are widely studied and used for compression. We propose a novel adaptive switched predictionbased scheme, which efficiently exploits temporal correlations in addition to spatial, and spectral correlations. The predictor switching uses a threshold, which is chosen based on the residual error distribution of already encoded band. Hence, our method does not need any overhead to be transmitted. The proposed scheme outperforms other stateoftheart methods in bitrate, and the method is computationally efficient too.

Embedded to lossless coding of motion compensated prediction residuals in lossless video coding Lossless video coding is useful in applications where no loss of information or visual quality is tolerable. In embedded to lossless coding an encoded video stream can be decoded into any bit rate up to the lossless bit rate, which is quite useful in numerous applications. In this paper, the research we present is based on lossless video coding, which uses motion compensated prediction to eliminate temporal redundancy. We specifically address the problem of embedded to lossless coding of the motion compensated prediction residuals. Since the statistical properties of the residuals are different from still images, the best wavelet bases for still images do not perform well for residuals. Since residuals contain a higher portion of high frequency information in high motion regions, a fixed transform for the entire frame is not very efficient. We introduce a spatially adaptive wavelet transform method, which takes the local frame statistics into account before choosing the wavelet base. This transform technique provided the best performance for most of the test frames.