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Dictionary based surveillance image compression

机译：基于字典的监视图像压缩

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摘要

Common image compression techniques suitable for general purpose may be less effective for such specific applications as video surveillance. Since a stationed surveillance camera always targets at a fixed scene, its captured images exhibit high consistency in content or structure. In this paper, we propose a surveillance image compression technique via dictionary learning to fully exploit the constant characteristics of a target scene. This method transforms images over sparsely tailored over-complete dictionaries learned directly from image samples rather than a fixed one, and thus can approximate an image with fewer coefficients. A set of dictionaries trained off-line is applied for sparse representation. An adaptive image blocking method is developed so that the encoder can represent an image in a texture-aware way. Experimental results show that the proposed algorithm significantly outperforms JPEG and JPEG 2000 in terms of both quality of reconstructed images and compression ratio as well. (C) 2015 Elsevier Inc. All rights reserved.
机译：适用于通用目的的通用图像压缩技术对于诸如视频监控之类的特定应用可能不太有效。由于固定式监视摄像机始终以固定场景为目标，因此其捕获的图像在内容或结构上显示出很高的一致性。在本文中，我们提出了一种通过字典学习的监视图像压缩技术，以充分利用目标场景的恒定特征。该方法通过直接从图像样本而不是固定样本中学习的稀疏剪裁过完全词典来转换图像，从而可以用较少的系数近似图像。一组离线训练的词典用于稀疏表示。开发了一种自适应图像分块方法，以便编码器可以以纹理感知的方式表示图像。实验结果表明，该算法在重建图像质量和压缩率方面均明显优于JPEG和JPEG 2000。 （C）2015 Elsevier Inc.保留所有权利。

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