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| Tunnels play a critical role in civil transportation infrastructures, and keeping them in optimal operation conditions is of great significance. With the development of computer vision technology, manual inspection is gradually being replaced by automatic or interactive vision inspection systems. During the inspection, these systems usually need to process a massive number of images, which inevitably causes storage problem. Therefore, a variety of image compression approaches have been proposed. However, most of the existing methods overlook the property of high consistency and homogeneity of tunnel images and are not fully applicable to tunnel inspection scenarios. In this paper, we propose a content-aware sparse coding based method for tunnel image compression. Firstly, we train a dictionary for predefined image patches. Secondly, an adaptive sparse coding algorithm is designed by considering the diversity of image content. Specifically, coefficients with more and less non-zero elements are adaptively allocated for complex and plain image textures, respectively. Thirdly, a novel non-uniform quantization method is presented, which has been proved to improve effectively the coding performance. Experimental results show that our method enjoys better visual results and outperforms JPEG and JPEG2000 in terms of Peak-Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM) index in lower bitrates. |
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| Keywords:image compression;sparse coding;content-aware; consistency and homogeneity |
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