Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary
We introduce a new image coder which uses the Iteration Tuned and Aligned Dictionary (ITAD) as a transform to code image blocks taken over a regular grid. We establish experimentally that the ITAD structure results in lower-complexity representations that enjoy greater sparsity when compared to other recent dictionary structures. We show that this superior sparsity can be exploited successfully for compressing images belonging to specific classes of images (e.g., facial images). We further propose a global rate-distortion criterion that distributes the code bits across the various image blocks. Our evaluation shows that the proposed ITAD codec can outperform JPEG2000 by more than 2 dB at 0.25 bpp and by 0.5 dB at 0.45 bpp, accordingly producing qualitatively better reconstructions.