Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders
Accessibility to big training datasets together with current advances in computing power has emerged interest in the leverage of deep learning to address image compression. This needs to train and deploy separate networks for rate adaptation, which is impractical and extensive in terms of memory cos...
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| Published in: | Signal, image and video processing Vol. 17; no. 1; pp. 285 - 293 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Springer London
01.02.2023
Springer Nature B.V |
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| ISSN: | 1863-1703, 1863-1711 |
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| Abstract | Accessibility to big training datasets together with current advances in computing power has emerged interest in the leverage of deep learning to address image compression. This needs to train and deploy separate networks for rate adaptation, which is impractical and extensive in terms of memory cost and power consumption, especially for broad bitrate ranges. To deal with such limitation, the variable-rate compression methods use the Lagrange multiplier to control the Rate/Distortion trade-offs in order not to require retraining of the neural network for each rate. However, they do not make an optimized bit allocation for the eye-catching foreground details, and do not consider the different degree of attention that the human eye has to each area of the image. Thus, other deep learning-based image compression approaches, which could outperform the above ones, are replied on the use of additional information. In this paper, we present a loss-conditional autoencoder tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy variable-rate compression. Our framework is a neural network-based scheme able to automatically optimize coding parameters with multi-term perceptual loss function based on semantic-important structural SIMilarity index. To ensure the rate adaptation, we suggest modulating the compression network on the bitwidth of its activations by quantizing them according to several bitwidth values. Experiments are presented on the JPEG AI dataset in which our method achieves competitive and higher visual quality for the same compressed size, when compared to conventional codecs and related work. |
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| AbstractList | Accessibility to big training datasets together with current advances in computing power has emerged interest in the leverage of deep learning to address image compression. This needs to train and deploy separate networks for rate adaptation, which is impractical and extensive in terms of memory cost and power consumption, especially for broad bitrate ranges. To deal with such limitation, the variable-rate compression methods use the Lagrange multiplier to control the Rate/Distortion trade-offs in order not to require retraining of the neural network for each rate. However, they do not make an optimized bit allocation for the eye-catching foreground details, and do not consider the different degree of attention that the human eye has to each area of the image. Thus, other deep learning-based image compression approaches, which could outperform the above ones, are replied on the use of additional information. In this paper, we present a loss-conditional autoencoder tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy variable-rate compression. Our framework is a neural network-based scheme able to automatically optimize coding parameters with multi-term perceptual loss function based on semantic-important structural SIMilarity index. To ensure the rate adaptation, we suggest modulating the compression network on the bitwidth of its activations by quantizing them according to several bitwidth values. Experiments are presented on the JPEG AI dataset in which our method achieves competitive and higher visual quality for the same compressed size, when compared to conventional codecs and related work. |
| Author | Sebai, D. Shah, A. Ulah |
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| Cites_doi | 10.3390/app9173580 10.1016/j.neucom.2019.01.086 10.1109/ICCV.2019.00324 10.1109/ICASSP.2019.8683541 10.1109/CVPR.2018.00339 10.1109/DCC.2017.56 10.1109/LSP.2020.2970539 10.1109/MMSP48831.2020.9287130 10.1007/978-3-030-04167-0_9 10.1109/CVPR.2016.319 10.1109/MMSP53017.2021.9733550 10.1016/S0923-5965(01)00024-8 10.1109/CVPR.2017.577 10.1109/MMSP48831.2020.9287082 10.1109/CVPR.2018.00462 10.1109/ICCV.2015.123 10.1109/CVPRW50498.2020.00089 10.1109/30.125072 10.1109/TIP.2003.819861 10.1109/CVPRW50498.2020.00088 |
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| Keywords | Learning-based image compression Loss-conditional autoencoder Variable-rate compression Multi-term loss function Quantized autoencoders Only-Train-Once |
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| References | Luo, S., Yang, Y., Song, M.: DeepSIC: deep semantic image compression. In: International Conference on Neural Information Processing (2018) Theis, L., Shi, W., Cunningham, A., Huszar, F.: Lossy image compression with compressive autoencoders. In: International Conference on Learning Representations (2017) YangFHerranzLvan de WeijerJGuitinJAILopezAMozerovMVariable rate deep image compression with modulated autoencoderIEEE Signal Process. Lett.20202733133510.1109/LSP.2020.2970539 Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. In: International Conference on Learning Representations (2018) AgustssonEMentzerFTschannenMCavigelliLTimofteRBeniniLGoolLVSoft-to-hard vector quantization for end-toend learning compressible representationsAdv. Neural Inf. Process. Syst.20173011411151 Lee, W.-C., Chang, C.-P., Peng, W.-H., Hang, H.-M.: A hybrid layered image compressor with deep-learning technique. In: International Workshop on Multimedia Signal Processing (2020) Sebai, D.: Multi-rate deep semantic image compression with quantized modulated autoencoder. In: IEEE International Workshop on Multimedia Signal Processing (MMSP) (2021) Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.: Semantic perceptual image compression using deep convolution networks. IEEE Data Compression Conference (DCC) (2017) Zhou, B.L., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE Conference on Computer Vision and Pattern Recognition (2016) ChenZHeTLearning based facial image compression with semantic fidelity metricNeurocomputing2019338162510.1016/j.neucom.2019.01.086 Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. arXiv:1611.01704 (2016) Akbari, M., Liang, J., Han, J.: DSSLIC: deep semantic segmentation-based layered image compression. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019) Toderici, G., Vincent, D., Johnston, N., Hwang, S.J., Minnen, D., Shor, J., Covell, M.: Full resolution image compression with recurrent neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5306–5314 (2017) Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014) Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. https://authors.library.caltech.edu/7694 Hoang, T.M., Zhou, J., Fan, Y.: Image compression with encoder-decoder matched semantic segmentation. In: Conference on Computer Vision and Pattern Recognition Workshops (2020) Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: International Conference on Machine Learning, pp. 2922–2930 (2017) Zhou, J., Nakagawa, A., Kato, K., Wen, S., Kazui, K., Tan, Z.: Variable rate image compression method with dead-zone quantizer. In: Conference on Computer Vision and Pattern Recognition Workshops (2020) Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Gool, L.V.: Conditional probability models for deep image compression. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4394–4402 (2018) WangZBovikACSheikhHRSimoncelliEPImage quality assessment: from error visibility to structural similarityIEEE Trans. Image Process.20043460061210.1109/TIP.2003.819861 Choi, Y., El-Khamy, M., Lee, J.: Variable Rate Deep Image Compression With a Conditional autoencoder. In: International Conference on Computer Vision (ICCV) (2019) Wallace, G. K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), xviii–xxxiv (1992) Akyazi, P., Ebrahimi, T.: Learning-based image compression using convolutional autoencoder and wavelet decomposition. IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019) Ascenso, J., Akyazi, P.: MPEG AI image coding common test conditions. In: 84th\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$84^{th}$$\end{document} JPEG meeting (ISO/IEC JTC 1/SC29/WG1, document N84035), Brussels, Geneva: ISO, 13–19 July (2019) He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision (ICCV) (2015) Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Sig. Process.: Image Commun. 17(1), 3–48 (2002) Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: Learning convolutional networks for content weighted image compression. In: IEEE Conference on Computer Vision and Pattern Recognition (2018) WangCHanYWangWAn end-to-end deep learning image compression framework based on semantic analysisAppl. Sci.2019917358010.3390/app9173580 Chen, Z., Tianyu, H.: Learning based facial image compression with semantic fidelity metric. Neurocomputing 338, 16–25 (2019) Lee, J., Cho, S., Beack, S.-K.: Context-adaptive entropy model for end-to-end optimized image compression. In: International Conference on Learning Representations (2019) Lin, J., Akbari, M., Fu, H., Zhang, Q.,Wang, S., Liang, J., Liu, D., Liang, F., Zhang, G., Tu, C.: Learned variable-rate multi-frequency image compression using modulated generalized octave convolution. In: International Workshop on Multimedia Signal Processing (2020) Mahalingaiah, K., Sharma, H., Kaplish, P., Cheng, I.: Semantic learning for image compression (SLIC) Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.: Semantic perceptual image compression using deep convolution networks. In: Data Compression Conference (DCC) (2017) MinnenDBalléJTodericiGDJoint autoregressive and hierarchical priors for learned image compressionAdv. Neural Inf. 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| References_xml | – reference: Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Sig. Process.: Image Commun. 17(1), 3–48 (2002) – reference: YangFHerranzLvan de WeijerJGuitinJAILopezAMozerovMVariable rate deep image compression with modulated autoencoderIEEE Signal Process. Lett.20202733133510.1109/LSP.2020.2970539 – reference: Fraunhofer Heinrich Hertz Institute.: VVC official test model VTM. https://vcgit.hhi.fraunhofer.de/jvet/VVCSoftware – reference: Dosovitskiy, A., Djolonga, J.: You only train once: Loss-conditional training of deep networks In: International Conference on Learning Representations (2020) – reference: Lee, J., Cho, S., Beack, S.-K.: Context-adaptive entropy model for end-to-end optimized image compression. In: International Conference on Learning Representations (2019) – reference: Akbari, M., Liang, J., Han, J.: DSSLIC: deep semantic segmentation-based layered image compression. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019) – reference: Wallace, G. K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), xviii–xxxiv (1992) – reference: Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. In: International Conference on Learning Representations (2018) – reference: AgustssonEMentzerFTschannenMCavigelliLTimofteRBeniniLGoolLVSoft-to-hard vector quantization for end-toend learning compressible representationsAdv. Neural Inf. Process. Syst.20173011411151 – reference: Lee, W.-C., Chang, C.-P., Peng, W.-H., Hang, H.-M.: A hybrid layered image compressor with deep-learning technique. In: International Workshop on Multimedia Signal Processing (2020) – reference: Chen, Z., Tianyu, H.: Learning based facial image compression with semantic fidelity metric. Neurocomputing 338, 16–25 (2019) – reference: Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.: Semantic perceptual image compression using deep convolution networks. In: Data Compression Conference (DCC) (2017) – reference: Ascenso, J., Akyazi, P.: MPEG AI image coding common test conditions. In: 84th\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$84^{th}$$\end{document} JPEG meeting (ISO/IEC JTC 1/SC29/WG1, document N84035), Brussels, Geneva: ISO, 13–19 July (2019) – reference: WangZBovikACSheikhHRSimoncelliEPImage quality assessment: from error visibility to structural similarityIEEE Trans. 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In: IEEE Conference on Computer Vision and Pattern Recognition (2016) – reference: He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision (ICCV) (2015) – reference: Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: International Conference on Machine Learning, pp. 2922–2930 (2017) – reference: Sebai, D.: Multi-rate deep semantic image compression with quantized modulated autoencoder. In: IEEE International Workshop on Multimedia Signal Processing (MMSP) (2021) – reference: MinnenDBalléJTodericiGDJoint autoregressive and hierarchical priors for learned image compressionAdv. Neural Inf. Process. Syst.2018311079410803 – reference: Theis, L., Shi, W., Cunningham, A., Huszar, F.: Lossy image compression with compressive autoencoders. In: International Conference on Learning Representations (2017) – reference: Luo, S., Yang, Y., Song, M.: DeepSIC: deep semantic image compression. In: International Conference on Neural Information Processing (2018) – reference: Bellard, F.: BPG image format. https://bellard.org/bpg (2014) – reference: Hoang, T.M., Zhou, J., Fan, Y.: Image compression with encoder-decoder matched semantic segmentation. In: Conference on Computer Vision and Pattern Recognition Workshops (2020) – reference: Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. https://authors.library.caltech.edu/7694/ – reference: Lin, J., Akbari, M., Fu, H., Zhang, Q.,Wang, S., Liang, J., Liu, D., Liang, F., Zhang, G., Tu, C.: Learned variable-rate multi-frequency image compression using modulated generalized octave convolution. In: International Workshop on Multimedia Signal Processing (2020) – reference: Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. arXiv:1611.01704 (2016) – reference: Akyazi, P., Ebrahimi, T.: Learning-based image compression using convolutional autoencoder and wavelet decomposition. IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019) – reference: ChenZHeTLearning based facial image compression with semantic fidelity metricNeurocomputing2019338162510.1016/j.neucom.2019.01.086 – reference: Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: Learning convolutional networks for content weighted image compression. In: IEEE Conference on Computer Vision and Pattern Recognition (2018) – reference: WangCHanYWangWAn end-to-end deep learning image compression framework based on semantic analysisAppl. 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| SubjectTerms | Adaptation Codec Computer Imaging Computer Science Datasets Deep learning Image coding Image compression Image Processing and Computer Vision Image quality Lagrange multiplier Machine learning Methods Multimedia Information Systems Neural networks Original Paper Pattern Recognition and Graphics Semantic analysis Semantics Signal,Image and Speech Processing Vision |
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| Title | Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders |
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