{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unified-multivariate-gaussian-mixture-for","title":"Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression","arxiv_id":"2203.10897","date":"2022-03-21","proceeding":"CVPR 2022 1","authors":["Xiaosu Zhu","Jingkuan Song","Lianli Gao","Feng Zheng","Heng Tao Shen"],"abstract":"Modeling latent variables with priors and hyperpriors is an essential problem in variational image compression. Formally, trade-off between rate and distortion is handled well if priors and hyperpriors precisely describe latent variables. Current practices only adopt univariate priors and process each variable individually. However, we find inter-correlations and intra-correlations exist when observing latent variables in a vectorized perspective. These findings reveal visual redundancies to improve rate-distortion performance and parallel processing ability to speed up compression. This encourages us to propose a novel vectorized prior. Specifically, a multivariate Gaussian mixture is proposed with means and covariances to be estimated. Then, a novel probabilistic vector quantization is utilized to effectively approximate means, and remaining covariances are further induced to a unified mixture and solved by cascaded estimation without context models involved. Furthermore, codebooks involved in quantization are extended to multi-codebooks for complexity reduction, which formulates an efficient compression procedure. Extensive experiments on benchmark datasets against state-of-the-art indicate our model has better rate-distortion performance and an impressive $3.18\\times$ compression speed up, giving us the ability to perform real-time, high-quality variational image compression in practice. Our source code is publicly available at \\url{https://github.com/xiaosu-zhu/McQuic}.","url_abs":"https://arxiv.org/abs/2203.10897v1","url_pdf":"https://arxiv.org/pdf/2203.10897v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unified-multivariate-gaussian-mixture-for","repo_url":"https://github.com/xiaosu-zhu/McQuic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"image-compression","task_name":"Image Compression"},{"task_slug":"quantization","task_name":"Quantization"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.10897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10897"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/xiaosu-zhu/McQuic","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_honours":1,"ran_draft_wrong":2,"ran":1,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c92c27c924b517e8","entry":"get_2d_sincos_pos_embed","repo":"xiaosu-zhu/McQuic","repo_kind":"official","path":"mcquic/modules/generator.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/modules/generator.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c92c27c924b517e8"}},{"code_sha256_prefix":"03310bba324ae4fb","entry":"modulate","repo":"xiaosu-zhu/McQuic","repo_kind":"official","path":"mcquic/modules/generator.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/modules/generator.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"03310bba324ae4fb"}},{"code_sha256_prefix":"7257fdf155f5ef45","entry":"normalize_tensor","repo":"xiaosu-zhu/McQuic","repo_kind":"official","path":"mcquic/loss/lpips.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/loss/lpips.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7257fdf155f5ef45"}},{"code_sha256_prefix":"e268053216b1dd62","entry":"spatial_average","repo":"xiaosu-zhu/McQuic","repo_kind":"official","path":"mcquic/loss/lpips.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/loss/lpips.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"code_sha256_prefix":"d789e18c19c20aa0","entry":"build","repo":"xiaosu-zhu/McQuic","repo_kind":"official","path":"mcquic/modules/builder.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/modules/builder.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d789e18c19c20aa0"}},{"code_sha256_prefix":"665d8a4e8f673a4c","entry":"get_2d_sincos_pos_embed_from_grid","repo":"xiaosu-zhu/McQuic","repo_kind":"official","path":"mcquic/modules/generator.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/modules/generator.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"665d8a4e8f673a4c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}