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Low bit-rate cloud-based image coding in the wavelet transform domain

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Abstract

The rapid growth of image resources on the Internet makes it possible to find some highly correlated images on some Web sites when people plan to transmit an image over the Internet. This study proposes a low bit-rate cloud-based image coding scheme, which utilizes cloud resources to implement image coding. Multiple- discrete wavelet transform was adopted to decompose the input image into a low-frequency sub-band and several high-frequency sub-bands. The low-frequency sub-band image was used to retrieve highly correlated images (HCOIs) in the cloud. The highly correlated regions in the HCOIs were used to reconstruct the high-frequency sub-bands at the decoder to save bits. The final reconstructed image was generated using multiple inverse wavelet transform from a decompressed low-frequency sub-band and reconstructed high-frequency sub-bands. The experimental results showed that the coding scheme performed well, especially at low bit rates. The peak signal-to-noise ratio of the reconstructed image can gain up to 7 and 1.69 dB over JPEG and JPEG2000 under the same compression ratio, respectively. By utilizing the cloud resources, our coding scheme showed an obvious advantage in terms of visual quality. The details in the image can be well reconstructed compared with both JPEG, JPEG2000, and intracoding of HEVC.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61771220), the National Natural Science Foundation of China (No. 61631009) and the Graduate Innovation Fund of Jilin University. The authors would like to thank the authors of reference [7] for their works.

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  1. College of Communication Engineering, Jilin University, No. 5372 of Nanhu Road, Changchun, Jilin, China

    Chuxi Yang, Yan Zhao & Shigang Wang

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  1. Chuxi Yang
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  2. Yan Zhao
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  3. Shigang Wang
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Correspondence to Yan Zhao.

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Yang, C., Zhao, Y. & Wang, S. Low bit-rate cloud-based image coding in the wavelet transform domain. SIViP 12, 1437–1445 (2018). https://doi.org/10.1007/s11760-018-1299-4

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  • DOI: https://doi.org/10.1007/s11760-018-1299-4

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