Synonymous Variational Inference for Perceptual Image Compression
Authors: Zijian Liang, Kai Niu, Changshuo Wang, Jin Xu, Ping Zhang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate comparable rate-distortion-perception performance using a single progressive SIC codec, thus verifying the effectiveness of our proposed analysis method. |
| Researcher Affiliation | Academia | 1Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 2Peng Cheng Laboratory, Shenzhen, China 3State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China. Correspondence to: Kai Niu <EMAIL>. |
| Pseudocode | No | The paper describes methods and processes through text and diagrams (e.g., Figure 1, Figure 2, Figure 3), but it does not include a dedicated section or figure explicitly labeled as 'Pseudocode' or 'Algorithm' with structured, code-like steps. |
| Open Source Code | Yes | We will upload code for reproducing our results to the repository at https://github.com/ZJLiang6412/ Synonymous Image Compression. |
| Open Datasets | Yes | We randomly select 100,000 images from the Open Images V6 dataset (Kuznetsova et al., 2020) as the training data... We evaluate our models with the test set of CLIC2020 (Toderici et al., 2020), the validation set of DIV2K (Agustsson & Timofte, 2017), and the Kodak dataset 4. 4Kodak Photo CD dataset, URL http://r0k.us/graphics/kodak/. |
| Dataset Splits | Yes | We randomly select 100,000 images from the Open Images V6 dataset (Kuznetsova et al., 2020) as the training data... We evaluate our models with the test set of CLIC2020 (Toderici et al., 2020), the validation set of DIV2K (Agustsson & Timofte, 2017), and the Kodak dataset. |
| Hardware Specification | No | The paper mentions training models and performing experiments but does not specify any particular hardware components such as GPU models, CPU types, or memory sizes used for these computations. |
| Software Dependencies | No | The paper mentions implementing components using the Swin Transformer and utilizing the AdamW optimizer but does not specify version numbers for any software libraries, frameworks, or languages (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We train our model for 1 x 10^6 iterations with a batch size of 16, a learning rate of 1 x 10^-4, and the AdamW optimizer with a weight decay of 5 x 10^-5. Table 1 presents these hyperparameters of our progressive SIC model, which are configured empirically. We empirically set a = 4, b = 64 in this constraint. |