Ultra high resolution image coding and ROI viewing using line-based backward coding of wavelet trees (L-BCWT)

Viewing high quality regions of interest (ROI) from compressed bit streams of high resolution images is a desirable but extremely challenging goal. Current image formats used for such viewing commonly use tile based compression and suffer from tile-boundary artifacts. They also have a larger memory requirement because they maintain redundant scaled-down versions of the original image to provide progressive-of-resolution capabilities. We present here a novel codec based on line-based backward coding of wavelet trees (L-BCWT) that has been specifically designed to address these difficulties. With L-BCWT, only a fraction of the compressed image data is in memory at any given time while providing ROI and progressive-of-resolution capabilities. The performance of LBCWT is demonstrated with histology images on the order of billions of pixels or multi-gigabytes of raw data.