Rate-Distortion Optimized Image Compression Using Generalized Principal Component Analysis

A novel image compression algorithm based on generalized principal component analysis (GPCA) is proposed in this work. Each image block is first classified into a subspace and is represented with a linear combination of the basis vectors for the subspace. Therefore, the encoded information consists of subspace indices, basis vectors and transform coefficients. We adopt a vector quantization scheme and a predictive partial matching scheme to encode subspace indices and basis vectors, respectively. We also propose a rate-distortion optimized quantizer to encode transform coefficients efficiently. Simulation results demonstrate that the proposed algorithm provides better compression performance than JPEG, especially at low bitrates.