{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:16:44Z","timestamp":1760347004565,"version":"3.41.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T00:00:00Z","timestamp":1626912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., JPEG) of the base grayscale image can be integrated into the network in an end-to-end manner. To further reduce the size of the residual image, we present a specific layer to enhance Sparsity Enforcing Priors (SEP), thus leading to negligible storage space. The proposed method allows color embedding on a sparse residual image while keeping a high, 35dB PSNR on average. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts in terms of image quality and tolerability to compression.<\/jats:p>","DOI":"10.1145\/3451993","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T14:44:29Z","timestamp":1626965069000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Invertible Grayscale with Sparsity Enforcing Priors"],"prefix":"10.1145","volume":"17","author":[{"given":"Yong","family":"Du","sequence":"first","affiliation":[{"name":"Ocean University of China, Qingdao, China"}]},{"given":"Yangyang","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"given":"Taizhong","family":"Ye","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"given":"Qiang","family":"Wen","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"given":"Chufeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Guangzhou, China"}]},{"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[{"name":"Ocean University of China, Qingdao, China"}]},{"given":"Guoqiang","family":"Han","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"given":"Shengfeng","family":"He","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2017.01.010"},{"key":"e_1_2_2_2_1","volume-title":"Simoncelli","author":"Ball\u00e9 Johannes","year":"2017","unstructured":"Johannes Ball\u00e9 , Valero Laparra , and Eero P . Simoncelli . 2017 . End-to-end optimized image compression. In ICLR. Johannes Ball\u00e9, Valero Laparra, and Eero P. Simoncelli. 2017. End-to-end optimized image compression. In ICLR."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.55"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2380172"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186822.1073241"},{"key":"e_1_2_2_7_1","unstructured":"Will Grathwohl Dami Choi Yuhuai Wu Geoffrey Roeder and David Duvenaud. 2018. Backpropagation through the void: Optimizing control variates for black-box gradient estimation. In ICLR.  Will Grathwohl Dami Choi Yuhuai Wu Geoffrey Roeder and David Duvenaud. 2018. Backpropagation through the void: Optimizing control variates for black-box gradient estimation. In ICLR."},{"key":"e_1_2_2_8_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770\u2013778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770\u2013778."},{"volume-title":"Perceptual losses for real-time style transfer and super-resolution","author":"Johnson Justin","key":"e_1_2_2_9_1","unstructured":"Justin Johnson , Alexandre Alahi , and Li Fei-Fei . 2016. Perceptual losses for real-time style transfer and super-resolution . In ECCV. Springer , 694\u2013711. Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In ECCV. Springer, 694\u2013711."},{"key":"e_1_2_2_10_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A method for stochastic optimization. In ICLR. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR."},{"key":"e_1_2_2_11_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-encoding variational bayes. In ICLR. Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational bayes. In ICLR."},{"key":"e_1_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Gustav Larsson Michael Maire and Gregory Shakhnarovich. 2016. Learning representations for automatic colorization. In ECCV. 577\u2013593.  Gustav Larsson Michael Maire and Gregory Shakhnarovich. 2016. Learning representations for automatic colorization. In ECCV. 577\u2013593.","DOI":"10.1007\/978-3-319-46493-0_35"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186562.1015780"},{"key":"e_1_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Mu Li Wangmeng Zuo Shuhang Gu Debin Zhao and David Zhang. 2018. Learning convolutional networks for content-weighted image compression. In CVPR. 3214\u20133223.  Mu Li Wangmeng Zuo Shuhang Gu Debin Zhao and David Zhang. 2018. Learning convolutional networks for content-weighted image compression. In CVPR. 3214\u20133223.","DOI":"10.1109\/CVPR.2018.00339"},{"key":"e_1_2_2_15_1","volume-title":"Kingma","author":"Louizos Christos","year":"2018","unstructured":"Christos Louizos , Max Welling , and Diederik P . Kingma . 2018 . Learning sparse neural networks through regularization. In ICLR. Christos Louizos, Max Welling, and Diederik P. Kingma. 2018. Learning sparse neural networks through regularization. In ICLR."},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2407156.2407174"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0732-6"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/2383847.2383887"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.388"},{"key":"e_1_2_2_20_1","volume-title":"The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712","author":"Maddison Chris J.","year":"2016","unstructured":"Chris J. Maddison , Andriy Mnih , and Yee Whye Teh . 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 ( 2016 ). Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 (2016)."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2000.900912"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2381255.2381267"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1179352.1142017"},{"key":"e_1_2_2_24_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In ICLR.  Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In ICLR."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/79.952804"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2008.01116.x"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295023"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/30.125072"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"e_1_2_2_30_1","first-page":"6","article-title":"Invertible grayscale","volume":"37","author":"Xia Menghan","year":"2018","unstructured":"Menghan Xia , Xueting Liu , and Tien-Tsin Wong . 2018 . Invertible grayscale . ACM Trans Graph. 37 , 6 (Dec. 2018), 246:1\u2013246:10. Menghan Xia, Xueting Liu, and Tien-Tsin Wong. 2018. Invertible grayscale. ACM Trans Graph. 37, 6 (Dec. 2018), 246:1\u2013246:10.","journal-title":"ACM Trans Graph."},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508404"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2994148"},{"key":"e_1_2_2_33_1","volume-title":"Efros","author":"Zhang Richard","year":"2016","unstructured":"Richard Zhang , Phillip Isola , and Alexei A . Efros . 2016 . Colorful image colorization. In ECCV. 649\u2013666. Richard Zhang, Phillip Isola, and Alexei A. Efros. 2016. Colorful image colorization. In ECCV. 649\u2013666."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073703"},{"key":"e_1_2_2_35_1","volume-title":"Efros","author":"Zhu Jun-Yan","year":"2017","unstructured":"Jun-Yan Zhu , Taesung Park , Phillip Isola , and Alexei A . Efros . 2017 . Unpaired image-to-image translation using cycle-consistent adversarial networkss. In ICCV. Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networkss. In ICCV."}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451993","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3451993","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:03:00Z","timestamp":1750197780000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,22]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3451993"],"URL":"https:\/\/doi.org\/10.1145\/3451993","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"type":"print","value":"1551-6857"},{"type":"electronic","value":"1551-6865"}],"subject":[],"published":{"date-parts":[[2021,7,22]]},"assertion":[{"value":"2020-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}