Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding
Authors: Yichi Zhang, Zhihao Duan, Ming Lu, Dandan Ding, Fengqing Zhu, Zhan Ma
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superior performance of CLIC over state-of-the-art works: when optimized using MSE, it outperforms VVC by about 10% BD-Rate in three widely-used benchmark datasets; when optimized using MSSSIM, it saves more than 50% BD-Rate over VVC. Our CLIC offers a new way to generate compact representations for image compression, which also provides a novel direction along the line of LIC development. The paper includes sections like 'Experimental Results', 'Quantitative Results', 'Qualitative Visualization', and 'Ablation Study' with detailed performance tables and figures. |
| Researcher Affiliation | Academia | 1Hangzhou Normal University, Hangzhou, Zhejiang, China 2Purdue University, West Lafayette, Indiana, U.S. 3 Nanjing University, Nanjing, Jiangsu, China |
| Pseudocode | No | The paper describes algorithmic steps for clustering in prose (steps 1-5 in the 'Contextual Clustering' section), but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | We use Flicker2W (Liu et al. 2020b) and LIU4K (Liu et al. 2020a) as our training sets. The paper also provides links to the testing datasets: Kodak2 (https://r0k.us/graphics/kodak/), Tecnick3 (https://tecnick.com/?aiocp%20dp=testimages), and CLIC 20224 (http://compression.cc/). |
| Dataset Splits | Yes | Following Liu et al. (Liu et al. 2020b), 99% of the images were used for training, and the remaining 1% were used for validation. |
| Hardware Specification | Yes | All training was performed on a computer with an RTX4090 GPU, i9-13900K CPU, and 64G RAM. Ablation experiments were performed on a computer with an RTX3090 GPU, i7-9700K CPU, and 64G RAM. |
| Software Dependencies | No | The paper mentions specific optimizers (Adam) and methods (U-Q, DS-Q) with citations, but does not specify versions of programming languages, libraries (e.g., PyTorch, TensorFlow), or other software dependencies. |
| Experiment Setup | Yes | Following the settings of Compress AI (B egaint et al. 2020), we set λ {18, 35, 67, 130, 250, 483} 10 4 for MSE optimized model and λ {2.40, 4.58, 8.73, 16.64, 31.73, 60.50} for MS-SSIM optimized model. We trained each model with Adam optimizer (Kingma and Ba 2014) with β1 = 0.9, β2 = 0.999. Each model was trained for 300 epochs with a batch size of 8 and an initial learning rate of 1e 4. We used the Reduce LRon Plateau lr scheduler with a patience of 5 and a factor of 0.5. The first 150 epochs are called Stage 1 and the next 150 epochs are called Stage 2. When switching from Stage 1 to Stage 2, the learning rate is newly set to 1e 4. |