  This paper shows how to reduce the computational cost for a variety of common
machine vision tasks by operating directly in the compressed domain,
particularly in the context of hardware acceleration. Pyramid Vector
Quantization (PVQ) is the compression technique of choice and its properties
are exploited to simplify Support Vector Machines (SVM), Convolutional Neural
Networks(CNNs), Histogram of Oriented Gradients (HOG) features, interest points
matching and other algorithms.
