Abstract
In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance. However, most learning-based image compression methods are unlabeled and do not consider image semantics or content when optimizing the model. In fact, human eyes have different sensitivities to different content, so the image content also needs to be considered. In this paper, we propose a content-oriented image compression method, which handles different kinds of image contents with different strategies. Extensive experiments show that the proposed method achieves competitive subjective results compared with state-of-the-art end-to-end learned image compression methods or classic methods.
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References
Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Van Gool, L.: Generative adversarial networks for extreme learned image compression. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 221–231 (2019). https://doi.org/10.1109/ICCV.2019.00031
Akutsu, H., Suzuki, A., Zhong, Z., Aizawa, K.: Ultra low bitrate learned image compression by selective detail decoding. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 524–528 (2020). https://doi.org/10.1109/CVPRW50498.2020.00067
Ballé, J., Laparra, V., Simoncelli, E.: End-to-end optimized image compression. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)
Ballé, J., Minnen, D., Singh, S., Hwang, S., Johnston, N.: Variational image compression with a scale hyperprior. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
Blau, Y., Michaeli, T.: Rethinking lossy compression: the rate-distortion-perception tradeoff. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 675–685 (2019)
BPG: Bpg image format (2014). https://bellard.org/bpg/. Accessed 7 Mar 2022
Cai, C., Chen, L., Zhang, X., Gao, Z.: End-to-end optimized ROI image compression. IEEE Trans. Image Process. 29, 3442–3457 (2020). https://doi.org/10.1109/TIP.2019.2960869
Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: YOLO-face: a real-time face detector. Vis. Comput. 37(4), 805–813 (2020). https://doi.org/10.1007/s00371-020-01831-7
Cui, Z., Wang, J., Gao, S., Guo, T., Feng, Y., Bai, B.: Asymmetric gained deep image compression with continuous rate adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10532–10541 (2021)
Dash, S., Kumaravelu, G., Naganoor, V., Raman, S.K., Ramesh, A., Lee, H.: Compressnet: Generative compression at extremely low bitrates. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2314–2322 (2020). https://doi.org/10.1109/WACV45572.2020.9093415
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034 (2015). https://doi.org/10.1109/ICCV.2015.123
Hongyang, G., Hao, Y., Zhengyang, W., Shuiwang, J.: Pixel deconvolutional networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard gan. In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)
Kodak, E.: Kodak lossless true color image suite: photocd pcd0992 (1993). http://r0k.us/graphics/kodak/. Accessed 7 Mar 2022
Lee, J., Cho, S., Beack, S.K.: Context-adaptive entropy model for end-to-end optimized image compression. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
Li, C., Wand, M.: Precomputed real-time texture synthesis with markovian generative adversarial networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 702–716. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_43
Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: Learning convolutional networks for content-weighted image compression. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3214–3223 (2018). https://doi.org/10.1109/CVPR.2018.00339
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., Zhou, J.: Structure-preserving super resolution with gradient guidance, pp. 7766–7775 (2020). https://doi.org/10.1109/CVPR42600.2020.00779
Mentzer, F., Toderici, G., Tschannen, M., Agustsson, E.: High-fidelity generative image compression. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
Minnen, D., Ballé, J., Toderici, G.: Joint autoregressive and hierarchical priors for learned image compression. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 10794–10803 (2018)
Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2922–2930. PMLR (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
David, S.T.: Jpeg 2000: image compression fundamentals, standards and practice. J. Electron. Imaging 11(2), 286 (2002)
Wallace, G.: The jpeg still picture compression standard. IEEE Trans. Consum. Electron. 38(1), 18–34 (1992). https://doi.org/10.1109/30.125072
Wang, X., et al.: Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, vol. 2, pp. 1398–1402 (2003). https://doi.org/10.1109/ACSSC.2003.1292216
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Zhang, X., Wu, X.: Attention-guided image compression by deep reconstruction of compressive sensed saliency skeleton. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13354–13364 (2021)
Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.R.: Residual non-local attention networks for image restoration. ArXiv abs/1903.10082 (2019)
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Li, M., Gao, S., Feng, Y., Shi, Y., Wang, J. (2022). Content-Oriented Learned Image Compression. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13679. Springer, Cham. https://doi.org/10.1007/978-3-031-19800-7_37
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