Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary
Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary
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DOI：
10.1109/jstsp.2011.2135332
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发表时间：
2011-04
影响因子：
7.5
通讯作者：
J. Zepeda;C. Guillemot;Ewa Kijak
中科院分区：
文献类型：
--
作者：
J. Zepeda;C. Guillemot;Ewa Kijak
我们介绍了一种新的图像编码器，它使用迭代调整和对齐字典（ITAD）作为一个变换的代码图像块采取了一个规则的网格。我们建立实验，ITAD结构的结果在较低的复杂性表示，享受更大的稀疏性相比，其他最近的字典结构。我们表明，这种上级稀疏性可以成功地用于压缩属于特定类别的图像（例如，面部图像）。我们进一步提出了一个全球性的率失真标准，分布在各个图像块的代码位。我们的评估表明，建议的ITAD编解码器可以优于JPEG 2000超过2分贝在0.25 bpp和0.5分贝在0.45 bpp，从而产生更好的质量重建。
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.