Fast Image Vector Quantization Using Sparse Oblique Regression Trees

Vector quantization is a fundamental model for image coding, but large codebooks require a long training and encoding time. This can be sped up with tree-structured codes. We propose the use of sparse oblique decision trees, which have hyperplane splits with few nonzero weights in the decision nodes. Such trees can be trained on a dataset of image patches using tree alternating optimization. Experimentally with different datasets, we show these trees consistently achieve a low distortion, close to that of a flat codebook, and a much faster encoding, exceeding other tree-structured vector quantizers.