Abstract
Recently, learning-based image compression methods that utilize convolutional neural layers have been developed rapidly. Rescaling modules such as batch normalization which are often used in convolutional neural networks do not operate adaptively for the various inputs. Therefore, Generalized Divisible Normalization (GDN) has been widely used in image compression to rescale the input features adaptively across both spatial and channel axes. However, the representation power or degree of freedom of GDN is severely limited. Additionally, GDN cannot consider the spatial correlation of an image. To handle the limitations of GDN, we construct an expanded form of the adaptive scaling module, named Expanded Adaptive Scaling Normalization (EASN). First, we exploit the swish function to increase the representation ability. Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation. Furthermore, we introduce an input map** function to give the module a higher degree of freedom. We demonstrate how our EASN works in an image compression network using the visualization results of the feature map, and we conduct extensive experiments to show that our EASN increases the rate-distortion performance remarkably, and even outperforms the VVC intra at a high bit rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
VVC VTM reference software. https://vcgit.hhi.fraunhofer.de/jvet/VVCSoftware_VTM
Agarap, A.F.: Deep learning using rectified linear units (RELU). ar**v preprint ar**v:1803.08375 (2018)
Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. ar**v preprint ar**v:1611.01704 (2016)
Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. ar**v preprint ar**v:1802.01436 (2018)
Bégaint, J., Racapé, F., Feltman, S., Pushparaja, A.: CompressAI: a PyTorch library and evaluation platform for end-to-end compression research. ar**v preprint ar**v:2011.03029 (2020)
Bellard, F.: BPG image format (2015). Signalprocessing: Imagecommunication
Chen, H., Gu, J., Zhang, Z.: Attention in attention network for image super-resolution. ar**v preprint ar**v:2104.09497 (2021)
Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Learned image compression with discretized Gaussian mixture likelihoods and attention modules. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7939–7948 (2020)
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, pp. 10532–10541 (2021)
CVPR2021: Workshop and challenge on learned image compression (2021). http://clic.compression.cc/2021/tasks/index.html
Dai, T., Cai, J., Zhang, Y., **a, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11065–11074 (2019)
Feichtenhofer, C., Pinz, A., Wildes, R.P.: Spatiotemporal multiplier networks for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4768–4777 (2017)
He, D., Zheng, Y., Sun, B., Wang, Y., Qin, H.: Checkerboard context model for efficient learned image compression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14771–14780 (2021)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ar**v preprint ar**v:1412.6980 (2014)
Kodak, E.: Kodak lossless true color image suite (PhotoCD PCD0992). http://r0k.us/graphics/kodak/
Lee, J., Cho, S., Beack, S.K.: Context-adaptive entropy model for end-to-end optimized image compression. ar**v preprint ar**v:1809.10452 (2018)
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
Minnen, D., Ballé, J., Toderici, G.D.: Joint autoregressive and hierarchical priors for learned image compression. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Minnen, D., Singh, S.: Channel-wise autoregressive entropy models for learned image compression. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 3339–3343. IEEE (2020)
Niu, B., et al.: Single image super-resolution via a holistic attention network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 191–207. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_12
Ohm, J.R., Sullivan, G.J.: Versatile video coding-towards the next generation of video compression. In: Picture Coding Symposium (2018)
Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: BAM: bottleneck attention module. ar**v preprint ar**v:1807.06514 (2018)
Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Signal Process.: Image Commun. 17(1), 3–48 (2002)
Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. ar**v preprint ar**v:1710.05941 (2017)
Sullivan, G.J., Ohm, J.R., Han, W.J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012). https://doi.org/10.1109/TCSVT.2012.2221191
Toderici, G., et al.: Variable rate image compression with recurrent neural networks. ar**v preprint ar**v:1511.06085 (2015)
Toderici, G., et al.: Full resolution image compression with recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5306–5314 (2017)
Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), xviii–xxxiv (1992)
Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems Computers, vol. 2, pp. 1398–1402 (2003). https://doi.org/10.1109/ACSSC.2003.1292216
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vis. (IJCV) 127(8), 1106–1125 (2019)
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286–301 (2018)
Zhou, L., Sun, Z., Wu, X., Wu, J.: End-to-end optimized image compression with attention mechanism. In: CVPR Workshops (2019)
Acknowledgement
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shin, C., Lee, H., Son, H., Lee, S., Lee, D., Lee, S. (2022). Expanded Adaptive Scaling Normalization for End to End 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 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-19790-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19789-5
Online ISBN: 978-3-031-19790-1
eBook Packages: Computer ScienceComputer Science (R0)