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Add .devcontainer/devcontainer.json to provide a Python 3 dev container (python:3.13.7-bookworm) with common-utils and python features; installs system libs and runs pip to install requirements. Add src/__pycache__/ to .gitignore. Add ipykernel and nbformat to requirements.txt for Jupyter support.
- Introduced LBT class for spatial redundancy exploitation using 2D Discrete Cosine Transform. - Implemented training, encoding, and decoding methods for LBT. - Added command-line arguments for block size, color transform, and perceptual quantization. - Updated requirements.txt to include 'torch' for PyTorch support.
…l problema. Con bloques grandes, la varianza puede ser muy pequeña, causando que 1/var explote y domine completamente el entrenamiento, haciendo que la transformación colapse a valores uniformes (gris). Learning rate adaptativo: lr = min(1e-2, 1.0 / N) - Para bloques de 32 (N=1024) esto da lr≈0.001, evitando inestabilidad. Gradient clipping - Previene explosión de gradientes con matrices grandes. Ahora la transformación solo optimiza la reconstrucción, manteniendo las matrices cercanas a la identidad (que es la inicialización) y convergiendo correctamente.
…compression - Introduced authorship section in the notebook. - Added a theoretical explanation of the Learning Block Transform (LBT) including model architecture and encoding flow. - Explained block size considerations and their impact on image processing. - Detailed the training process of the LBT model, including loss functions and optimization strategies. - Included information on quantization techniques and perceptual quantization. - Added implementation details for the LBT model in a new Python file.
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Buenas tardes. En nombre del grupo compuesto por:
Hacemos entrega de nuestro algoritmo LBT propuesto. |
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