Impulse Noise Immune Bayer Image Compression with direction estimation for Imaging Sensor As higher resolution sensors and fast readout sensors (FRS) emerge, the band width of pixel data to be transmitted from the sensor increases drastically in recent years. However, it becomes difficult to support the whole required band width, and compressing pixel data in the sensor can resolve this band width limitation. Currently there are few available Bayer compression algorithms for sensors. Even if we use the standard differential pulse coded modulation (DPCM), it is difficult to obtain the high quality image with meaningful compression ratio. We propose the new compression algorithm, Impulse Noise Immune Bayer Image Compression (INIBIC), which is a more efficient way to use it on sensor. Firstly, INIBIC is suitable for sensor owe to low complexity and low power. Secondly, INIBIC can accurately predict through direction estimation and minimize data loss at edges in images, and this accurate direction estimation induces high quality in image reconstruction even in higher compression ratio. Thirdly, INIBIC makes better compression through effective bitallocation for bad pixels (BPs), which come from sensor defects and result in much loss in DPCM.

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.