Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Contextual Video Compression
Authors: Jiahao Li, Bin Li, Yan Lu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method can significantly outperform the previous state-of-the-art (SOTA) deep video compression methods. |
| Researcher Affiliation | Industry | Jiahao Li, Bin Li, Yan Lu Microsoft Research Asia EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are at https://github.com/Deep MC-DCVC/DCVC. |
| Open Datasets | Yes | We use the training part in Vimeo-90k septuplet dataset [33] as our training data. ... The testing data includes HEVC Class B (1080P), C (480P), D (240P), E (720P) from the common test conditions [34] used by codec standard community. In addition, The 1080p videos from MCL-JCV[29] and UVG[35] datasets are also tested. |
| Dataset Splits | No | The paper mentions 'Training data' and 'Testing data' but does not provide specific details on validation dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | the actual inference time per 1080P frame is 857 ms for DCVC and 849 ms for DVCPro on P40 GPU |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | For the learning rate, it is set as 1e-4 at the start and 1e-5 at the fine-tuning stage. The training batch size is set as 4. For comparing DCVC with other methods, we follow [5] and train 4 models with different λ s {MSE: 256, 512, 1024, 2048; MS-SSIM: 8, 16, 32, 64}. |