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The compression of natural images and their transmission over multi-hop wireless networks still presents many challenges for the researchers and industry. In this paper we present a new block-based rate-distortion optimization algorithm that can encode efficiently the coefficients of a critically sampled, non-orthogonal or even redundant transform. The basic idea is to construct a specialized graph such that its minimum cut minimizes the energy functional. We propose to apply this technique for rate-distortion Lagrangian optimization in block-based subband image coding. The method yields good compression results compared to the state-of-art JPEG2000 codec, as well as a general improvement in visual quality.

Block-based graph-cut rate allocation for subband image compression and transmission over wireless networks