Image Coaddition with Temporally Varying Kernels

Large, multi-frequency imaging surveys, such as the Large Synaptic Survey Telescope (LSST), need to do near-real time analysis of very large datasets. This raises a host of statistical and computational problems where standard methods do not work. In this paper, we study a proposed method for combining stacks of images into a single summary image, sometimes referred to as a template. This task is commonly referred to as image coaddition. In part, we focus on a method proposed in previous work, which outlines a procedure for combining stacks of images in an online fashion in the Fourier domain. We evaluate this method by comparing it to two straightforward methods through the use of various criteria and simulations. Note that the goal is not to propose these comparison methods for use in their own right, but to ensure that additional complexity also provides substantially improved performance

Real-time filtering and detection of dynamics for compression of HDTV

The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals