{"id":"W4416117257","doi":"10.48550/arxiv.2504.04658","title":"3DM-WeConvene: Learned Image Compression with 3D Multi-Level Wavelet-Domain Convolution and Entropy Model","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China","keywords":"Prior probability; Entropy (arrow of time); Discrete wavelet transform; Convolution (computer science); Wavelet; Image compression; Pattern recognition (psychology); Entropy encoding; JPEG 2000","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003685572,0.0006658395,0.0007285374,0.0003043295,0.0003483676,0.0002056343,0.001964231,0.0004324748,0.00001871997],"category_scores_gemma":[0.00008791878,0.0005768209,0.000106765,0.0002438776,0.0002860718,0.0008825812,0.005953211,0.001227552,0.00002617323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001846478,"about_ca_system_score_gemma":0.0003364811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007322483,"about_ca_topic_score_gemma":0.00001260565,"domain_scores_codex":[0.9962772,0.0002572574,0.0006165546,0.001786174,0.0004756442,0.0005871971],"domain_scores_gemma":[0.9964216,0.0001514927,0.0005539118,0.002345476,0.0003008696,0.0002265917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001975279,0.004335018,0.07961363,0.006038358,0.001320439,0.0009518957,0.007475657,0.04103134,0.4048902,0.07520352,0.03469139,0.3424733],"study_design_scores_gemma":[0.001898334,0.000105262,0.01174271,0.001450348,0.00004420275,0.00002276582,0.0000287853,0.938445,0.03193222,0.01089672,0.002334702,0.001099004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05319218,0.0004253217,0.9433311,0.0005835177,0.000267982,0.0008650217,0.0002137919,0.0008968902,0.0002241451],"genre_scores_gemma":[0.133539,0.000461714,0.8639129,0.0004037299,0.00005455454,0.0002882743,0.0001683595,0.00004129509,0.00113021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8974136,"threshold_uncertainty_score":0.9996683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07514908471430566,"score_gpt":0.3167097470363515,"score_spread":0.2415606623220459,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}