Deep Contextual Video Compression
Authors: Jiahao Li, Bin Li, Yan Lu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {li.jiahao, libin, yanlu}@microsoft.com |
| 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}. |