Color Image Compression Using a Learned Dictionary of Pairs of Orthonormal Bases

We present a new color image compression algorithm for RGB images. In our previous work [6], we presented a machine learning technique to derive a dictionary of orthonormal basis triples for compact representation of an ensemble of color image patches from a training set. The patches were represented as 3D arrays of size n × n × 3, and our technique was based on the higher order singular value decomposition (HOSVD), an extension of the singular value decomposition (SVD) to higher order matrices [3]. The learning scheme exploited the cross-coupling between the R,G,B channels by implicitly learning a color space. In this paper, we show the benefits of representing color image patches as 2D matrices of size n × 3n and learning a dictionary of orthonormal basis pairs. We also present a method to leverage greater representational power from a learned dictionary without increasing its size. We present experimental results on all these variants of our method and compare them to JPEG and ...

• Type: Journal
• Year: 2011
• Where: DCC
• Authors: Xin Hou, Karthik S. Gurumoorthy, Ajit Rajwade
• Keywords: Color Image, Computer Graphics, DCC 2011, Orthonormal Basis, Singular Value Decomposition