{"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/bit-allocation-using-optimization","title":"Bit Allocation using Optimization","arxiv_id":"2209.09422","date":"2022-09-20","proceeding":null,"authors":["Tongda Xu","Han Gao","Chenjian Gao","Yuanyuan Wang","Dailan He","Jinyong Pi","Jixiang Luo","Ziyu Zhu","Mao Ye","Hongwei Qin","Yan Wang","Jingjing Liu","Ya-Qin Zhang"],"abstract":"In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC). First, we reveal a fundamental relationship between bit allocation in NVC and Semi-Amortized Variational Inference (SAVI). Specifically, we show that SAVI with GoP (Group-of-Picture)-level likelihood is equivalent to pixel-level bit allocation with precise rate \\& quality dependency model. Based on this equivalence, we establish a new paradigm of bit allocation using SAVI. Different from previous bit allocation methods, our approach requires no empirical model and is thus optimal. Moreover, as the original SAVI using gradient ascent only applies to single-level latent, we extend the SAVI to multi-level such as NVC by recursively applying back-propagating through gradient ascent. Finally, we propose a tractable approximation for practical implementation. Our method can be applied to scenarios where performance outweights encoding speed, and serves as an empirical bound on the R-D performance of bit allocation. Experimental results show that current state-of-the-art bit allocation algorithms still have a room of $\\approx 0.5$ dB PSNR to improve compared with ours. Code is available at \\url{https://github.com/tongdaxu/Bit-Allocation-Using-Optimization}.","url_abs":"https://arxiv.org/abs/2209.09422v5","url_pdf":"https://arxiv.org/pdf/2209.09422v5.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":"bit-allocation-using-optimization","repo_url":"https://github.com/tongdaxu/bit-allocation-using-optimization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"video-compression","task_name":"Video Compression"}],"methods":[{"method_slug":"variational-inference","method_name":"Variational Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2209.09422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.09422"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tongdaxu/bit-allocation-using-optimization","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"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":"5421d0b72850e0c6","entry":"Quantizator_SGA","repo":"tongdaxu/bit-allocation-using-optimization","repo_kind":"official","path":"5.2. Evaluation on Bit Allocation/I_model/models/priors.py","file_url":"https://github.com/tongdaxu/bit-allocation-using-optimization/blob/HEAD/5.2.%20Evaluation%20on%20Bit%20Allocation/I_model/models/priors.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5421d0b72850e0c6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}