{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:23:52Z","timestamp":1788495832478,"version":"build-2803163510"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62171429 and 62021001"],"award-info":[{"award-number":["62171429 and 62021001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["WK3490000006 and WK3490000007"],"award-info":[{"award-number":["WK3490000006 and WK3490000007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>Reconstruction-free image compression for machine vision aims to perform machine vision tasks directly on compressed-domain representations instead of reconstructed images. Existing reports have validated the feasibility of compressed-domain machine vision. However, we observe that when using recently learned compression models, the performance gap between compressed-domain and pixel-domain vision tasks is still large due to the lack of some natural inductive biases in pixel-domain convolutional neural networks. In this article, we attempt to address this problem by transferring knowledge from the pixel domain to the compressed domain. A knowledge transfer loss defined at both output level and feature level is proposed to narrow the gap between the compressed domain and the pixel domain. In addition, we modify neural networks for pixel-domain vision tasks to better suit compressed-domain inputs. Experimental results on several machine vision tasks show that the proposed method improves the accuracy of compressed-domain vision tasks significantly, which even outperforms learning on reconstructed images while avoiding the computational cost of image reconstruction.<\/jats:p>","DOI":"10.1145\/3678471","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T11:43:02Z","timestamp":1721216582000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Reconstruction-Free Image Compression for Machine Vision via Knowledge Transfer"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3705-1197","authenticated-orcid":false,"given":"Hanyue","family":"Tu","sequence":"first","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-6263","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1690-9836","authenticated-orcid":false,"given":"Wengang","family":"Zhou","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2188-3028","authenticated-orcid":false,"given":"Houqiang","family":"Li","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Johannes Ball\u00e9 Valero Laparra and Eero P. Simoncelli. 2015. Density modeling of images using a generalized normalization transformation. arXiv:1511.06281. Retrieved from https:\/\/arxiv.org\/abs\/1511.06281"},{"key":"e_1_3_1_3_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Ball\u00e9 Johannes","year":"2017","unstructured":"Johannes Ball\u00e9, Valero Laparra, and Eero P. Simoncelli. 2017. End-to-end optimized image compression. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_4_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Ball\u00e9 Johannes","year":"2018","unstructured":"Johannes Ball\u00e9, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. 2018. Variational image compression with a scale hyperprior. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_5_2","unstructured":"Jean B\u00e9gaint Fabien Racap\u00e9 Simon Feltman and Akshay Pushparaja. 2020. CompressAI: A PyTorch library and evaluation platform for end-to-end compression research. arXiv:2011.03029. Retrieved from https:\/\/arxiv.org\/abs\/2011.03029"},{"key":"e_1_3_1_6_2","unstructured":"Fabrice Bellard. 2014. BPG Image Format. Retrieved from https:\/\/bellard.org\/bpg\/"},{"key":"e_1_3_1_7_2","first-page":"3442","article-title":"End-to-end optimized ROI image compression","volume":"29","author":"Cai Chunlei","year":"2019","unstructured":"Chunlei Cai, Li Chen, Xiaoyun Zhang, and Zhiyong Gao. 2019. End-to-end optimized ROI image compression. IEEE Transactions on Image Processing 29 (2019), 3442\u20133457.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_8_2","first-page":"1","volume-title":"Proceedings of the 2022 IEEE International Conference on Visual Communications and Image Processing","author":"Chen Liuhong","year":"2022","unstructured":"Liuhong Chen, Heming Sun, Xiaoyang Zeng, and Yibo Fan. 2022. Learning from the NN-based compressed domain with deep feature reconstruction loss. In Proceedings of the 2022 IEEE International Conference on Visual Communications and Image Processing. IEEE, 1\u20135."},{"key":"e_1_3_1_9_2","first-page":"5008","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Chen Pengguang","year":"2021","unstructured":"Pengguang Chen, Shu Liu, Hengshuang Zhao, and Jiaya Jia. 2021. Distilling knowledge via knowledge review. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5008\u20135017."},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/TIP.2021.3058615","article-title":"End-to-end learnt image compression via non-local attention optimization and improved context modeling","volume":"30","author":"Chen Tong","year":"2021","unstructured":"Tong Chen, Haojie Liu, Zhan Ma, Qiu Shen, Xun Cao, and Yao Wang. 2021. End-to-end learnt image compression via non-local attention optimization and improved context modeling. IEEE Transactions on Image Processing 30 (2021), 3179\u20133191.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_11_2","first-page":"56","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Cheng Ka Leong","year":"2022","unstructured":"Ka Leong Cheng, Yueqi Xie, and Qifeng Chen. 2022. Optimizing image compression via joint learning with denoising. In Proceedings of the European Conference on Computer Vision. Springer, 56\u201373."},{"key":"e_1_3_1_12_2","volume-title":"Proceedings of the IEEE\/CVF Computer Vision and Pattern Recognition Conference (CVPR) Workshops","author":"Cheng Zhengxue","year":"2019","unstructured":"Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. 2019. Deep residual learning for image compression. In Proceedings of the IEEE\/CVF Computer Vision and Pattern Recognition Conference (CVPR) Workshops."},{"key":"e_1_3_1_13_2","first-page":"7939","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cheng Zhengxue","year":"2020","unstructured":"Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. 2020. Learned image compression with discretized gaussian mixture likelihoods and attention modules. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7939\u20137948."},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1109\/TIP.2022.3160602","article-title":"Scalable image coding for humans and machines","volume":"31","author":"Choi Hyomin","year":"2022","unstructured":"Hyomin Choi and Ivan V. Baji\u0107. 2022. Scalable image coding for humans and machines. IEEE Transactions on Image Processing 31 (2022), 2739\u20132754.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_15_2","volume-title":"Elements of Information Theory","author":"Cover Thomas M.","year":"1999","unstructured":"Thomas M. Cover. 1999. Elements of Information Theory. John Wiley & Sons."},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/PCS56426.2022.10018075","volume-title":"Proceedings of the 2022 Picture Coding Symposium","author":"Duan Zhihao","year":"2022","unstructured":"Zhihao Duan and Fengqing Zhu. 2022. Efficient feature compression for edge-cloud systems. In Proceedings of the 2022 Picture Coding Symposium. IEEE, 187\u2013191."},{"key":"e_1_3_1_17_2","first-page":"3484","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Ehrlich Max","year":"2019","unstructured":"Max Ehrlich and Larry S Davis. 2019. Deep residual learning in the jpeg transform domain. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3484\u20133493."},{"key":"e_1_3_1_18_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"31","author":"Gueguen Lionel","year":"2018","unstructured":"Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, and Jason Yosinski. 2018. Faster neural networks straight from jpeg. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 31."},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/PCS56426.2022.10018035","volume-title":"Proceedings of the 2022 Picture Coding Symposium","author":"Harell Alon","year":"2022","unstructured":"Alon Harell, Anderson De Andrade, and Ivan V. Baji\u0107. 2022. Rate-distortion in image coding for machines. In Proceedings of the 2022 Picture Coding Symposium. IEEE, 199\u2013203."},{"key":"e_1_3_1_20_2","first-page":"14771","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"He Dailan","year":"2021","unstructured":"Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, and Hongwei Qin. 2021. Checkerboard context model for efficient learned image compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 14771\u201314780."},{"key":"e_1_3_1_21_2","first-page":"2961","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"He Kaiming","year":"2017","unstructured":"Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross Girshick. 2017. Mask R-CNN. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2961\u20132969."},{"key":"e_1_3_1_22_2","first-page":"770","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 770\u2013778."},{"key":"e_1_3_1_23_2","first-page":"1921","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Heo Byeongho","year":"2019","unstructured":"Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, and Jin Young Choi. 2019. A comprehensive overhaul of feature distillation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 1921\u20131930."},{"key":"e_1_3_1_24_2","unstructured":"Geoffrey Hinton Oriol Vinyals and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv:1503.02531. Retrieved from https:\/\/arxiv.org\/abs\/1503.02531"},{"issue":"5786","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton Geoffrey E.","year":"2006","unstructured":"Geoffrey E. Hinton and Ruslan R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504\u2013507.","journal-title":"Science"},{"key":"e_1_3_1_26_2","first-page":"10116","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hou Yunzhong","year":"2020","unstructured":"Yunzhong Hou, Liang Zheng, and Stephen Gould. 2020. Learning to structure an image with few colors. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10116\u201310125."},{"key":"e_1_3_1_27_2","unstructured":"JPEG-AI. 2022. Final Call for Proposals for JPEG AI. SO\/IEC JTC 1\/SC29\/WG1 N100095."},{"issue":"6","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky Alex","year":"2017","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM 60, 6 (2017), 84\u201390.","journal-title":"Communications of the ACM"},{"key":"e_1_3_1_29_2","first-page":"1","volume-title":"Proceedings of the 2021 IEEE International Conference on Multimedia and Expo","author":"Le Nam","year":"2021","unstructured":"Nam Le, Honglei Zhang, Francesco Cricri, Ramin Ghaznavi-Youvalari, Hamed Rezazadegan Tavakoli, and Esa Rahtu. 2021. Learned image coding for machines: A content-adaptive approach. In Proceedings of the 2021 IEEE International Conference on Multimedia and Expo. IEEE, 1\u20136."},{"key":"e_1_3_1_30_2","first-page":"2117","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin, Piotr Doll\u00e1r, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2117\u20132125."},{"key":"e_1_3_1_31_2","first-page":"740","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In Proceedings of the European Conference on Computer Vision. Springer, 740\u2013755."},{"key":"e_1_3_1_32_2","first-page":"01","volume-title":"Proceedings of the 2021 International Conference on Visual Communications and Image Processing","author":"Liu Jinming","year":"2021","unstructured":"Jinming Liu, Heming Sun, and Jiro Katto. 2021. Learning in compressed domain for faster machine vision tasks. In Proceedings of the 2021 International Conference on Visual Communications and Image Processing. IEEE, 01\u201305."},{"key":"e_1_3_1_33_2","first-page":"331","volume-title":"Proceedings of the 2022 26th International Conference on Pattern Recognition","author":"Liu Jinming","year":"2022","unstructured":"Jinming Liu, Heming Sun, and Jiro Katto. 2022. Improving multiple machine vision tasks in the compressed domain. In Proceedings of the 2022 26th International Conference on Pattern Recognition. IEEE, 331\u2013337."},{"key":"e_1_3_1_34_2","first-page":"181","volume-title":"Proceedings of the 2022 Picture Coding Symposium","author":"Liu Jinming","year":"2022","unstructured":"Jinming Liu, Heming Sun, and Jiro Katto. 2022b. Semantic segmentation in learned compressed domain. In Proceedings of the 2022 Picture Coding Symposium. IEEE, 181\u2013185."},{"key":"e_1_3_1_35_2","first-page":"10012","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Liu Ze","year":"2021","unstructured":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021a. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 10012\u201310022."},{"key":"e_1_3_1_36_2","first-page":"2685","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Matsubara Yoshitomo","year":"2022","unstructured":"Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, and Stephan Mandt. 2022. Supervised compression for resource-constrained edge computing systems. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2685\u20132695."},{"key":"e_1_3_1_37_2","first-page":"1","volume-title":"Proceedings of the 2021 International Conference on Visual Communications and Image Processing","author":"Mei Yixin","year":"2021","unstructured":"Yixin Mei, Fan Li, Li Li, and Zhu Li. 2021. Learn a compression for objection detection-VAE with a bridge. In Proceedings of the 2021 International Conference on Visual Communications and Image Processing. IEEE, 1\u20135."},{"key":"e_1_3_1_38_2","volume-title":"Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS \u201918)","author":"Minnen David","year":"2018","unstructured":"David Minnen, Johannes Ball\u00e9, and George Toderici. 2018. Joint autoregressive and hierarchical priors for learned image compression. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS \u201918)."},{"key":"e_1_3_1_39_2","first-page":"3339","volume-title":"Proceedings of the 2020 IEEE International Conference on Image Processing","author":"Minnen David","year":"2020","unstructured":"David Minnen and Saurabh Singh. 2020. Channel-wise autoregressive entropy models for learned image compression. In Proceedings of the 2020 IEEE International Conference on Image Processing. IEEE, 3339\u20133343."},{"key":"e_1_3_1_40_2","first-page":"3967","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Park Wonpyo","year":"2019","unstructured":"Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho. 2019. Relational knowledge distillation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3967\u20133976."},{"key":"e_1_3_1_41_2","first-page":"8024","volume-title":"Advances in Neural Information Processing Systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-Performance deep learning library. In Advances in Neural Information Processing Systems. H. Wallach, H. Larochelle, A. Beygelzimer, F. d\u2019Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Curran Associates, Inc., 8024\u20138035. Retrieved from http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"e_1_3_1_42_2","unstructured":"Yichen Qian Zhiyu Tan Xiuyu Sun Ming Lin Dongyang Li Zhenhong Sun Hao Li and Rong Jin. 2020. Learning accurate entropy model with global reference for image compression. arXiv:2010.08321. Retrieved from https:\/\/arxiv.org\/abs\/2010.08321"},{"issue":"6","key":"e_1_3_1_43_2","first-page":"1137","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren Shaoqing","year":"2016","unstructured":"Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2016. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 6 (2016), 1137\u20131149.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_44_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Romero Adriana","year":"2015","unstructured":"Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2015. Fitnets: Hints for thin deep nets. In Proceedings of the International Conference on Learning Representations."},{"issue":"3","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky Olga","year":"2015","unstructured":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115, 3 (2015), 211\u2013252.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_1_46_2","first-page":"1874","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Shi Wenzhe","year":"2016","unstructured":"Wenzhe Shi, Jose Caballero, Ferenc Husz\u00e1r, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1874\u20131883."},{"issue":"5","key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/79.952804","article-title":"The jpeg 2000 still image compression standard","volume":"18","author":"Skodras Athanassios","year":"2001","unstructured":"Athanassios Skodras, Charilaos Christopoulos, and Touradj Ebrahimi. 2001. The jpeg 2000 still image compression standard. IEEE Signal Processing Magazine 18, 5 (2001), 36\u201358.","journal-title":"IEEE Signal Processing Magazine"},{"key":"e_1_3_1_48_2","first-page":"2686","volume-title":"Proceedings of the 2019 IEEE International Conference on Image Processing","author":"Suzuki Satoshi","year":"2019","unstructured":"Satoshi Suzuki, Motohiro Takagi, Kazuya Hayase, Takayuki Onishi, and Atsushi Shimizu. 2019. Image pre-transformation for recognition-aware image compression. In Proceedings of the 2019 IEEE International Conference on Image Processing. IEEE, 2686\u20132690."},{"key":"e_1_3_1_49_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive representation distillation. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_50_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Torfason Robert","year":"2018","unstructured":"Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool. 2018. Towards image understanding from deep compression without decoding. In Proceedings of the International Conference on Learning Representations."},{"issue":"1","key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"xviii","DOI":"10.1109\/30.125072","article-title":"The JPEG still picture compression standard","volume":"38","author":"Wallace Gregory K","year":"1992","unstructured":"Gregory K Wallace. 1992. The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics 38, 1 (1992), xviii\u2013xxxiv.","journal-title":"IEEE Transactions on Consumer Electronics"},{"key":"e_1_3_1_52_2","first-page":"3582","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Wang Zhenzhen","year":"2022","unstructured":"Zhenzhen Wang, Minghai Qin, and Yen-Kuang Chen. 2022. Learning from the CNN-based compressed domain. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 3582\u20133590."},{"key":"e_1_3_1_53_2","unstructured":"Yuxin Wu Alexander Kirillov Francisco Massa Wan-Yen Lo and Ross Girshick. 2019. Detectron2. Retrieved from https:\/\/github.com\/facebookresearch\/detectron2"},{"key":"e_1_3_1_54_2","first-page":"1740","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Xu Kai","year":"2020","unstructured":"Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, and Fengbo Ren. 2020. Learning in the frequency domain. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 1740\u20131749."},{"key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1145\/3394171.3413968","volume-title":"Proceedings of the 28th ACM International Conference on Multimedia","author":"Yang Zhaohui","year":"2020","unstructured":"Zhaohui Yang, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu, and Qi Tian. 2020. Discernible image compression. In Proceedings of the 28th ACM International Conference on Multimedia, 1561\u20131569."},{"key":"e_1_3_1_56_2","first-page":"18216","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Ye Juncheol","year":"2023","unstructured":"Juncheol Ye, Hyunho Yeo, Jinwoo Park, and Dongsu Han. 2023. AccelIR: Task-aware image compression for accelerating neural restoration. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 18216\u201318226."},{"key":"e_1_3_1_57_2","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv:1612.03928. Retrieved from https:\/\/arxiv.org\/abs\/1612.03928"},{"key":"e_1_3_1_58_2","first-page":"11953","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhao Borui","year":"2022","unstructured":"Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang. 2022. Decoupled knowledge distillation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 11953\u201311962."},{"key":"e_1_3_1_59_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhu Yinhao","year":"2022","unstructured":"Yinhao Zhu, Yang Yang, and Taco Cohen. 2022. Transformer-based transform coding. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_60_2","first-page":"17492","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zou Renjie","year":"2022","unstructured":"Renjie Zou, Chunfeng Song, and Zhaoxiang Zhang. 2022. The devil is in the details: Window-based attention for image compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 17492\u201317501."}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678471","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3678471","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:04:18Z","timestamp":1750277058000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,15]]},"references-count":59,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3678471"],"URL":"https:\/\/doi.org\/10.1145\/3678471","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,15]]},"assertion":[{"value":"2023-10-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}