How gabilodeau/inf8770 works
INF8770 is a collection of Jupyter notebook examples for a multimedia technologies course, rather than a single application pipeline. The principal workflow is a learner opening and running a notebook to explore a specific multimedia technique. The graph preserves the README’s notebook-based format and the major topic areas visible in the tree; source excerpts were limited, so it does not infer calls between notebooks or links from sample media files.
Main entry points: Caracteristiques acoustiques.ipynb Acoustic features · Calcul spectrogramme.ipynb Spectrograms · detect_peaks.py Peak detection · Gradients et extraction d'aretes sur une image.ipynb Image gradients
Components 33
Audio and signals
- Acoustic features
Caracteristiques acoustiques.ipynb - Spectrograms
Calcul spectrogramme.ipynb - Peak detection
detect_peaks.py
Image processing
- Image gradients
Gradients et extraction d'aretes sur une image.ipynb - Matrix gradients
Gradients sur matrice.ipynb - Image frequencies
Image et frequences.ipynb - Image surface
ImageCommeSurface.ipynb - Image quality
Evaluation de la qualite des images.ipynb - DCT and quantization
Transformee DCT et quantification.ipynb - KL transform
Transformee KL sur image.ipynb - Wavelet transform
Transformee en ondelettes.ipynb - Deblocking filter
FiltreAntiBlocs.ipynb - Floyd–Steinberg image
Floyd-Steinberg sur image.ipynb - Floyd–Steinberg matrix
Floyd-Steinberg sur matrice.ipynb
Coding and compression
- PCM coding
Codage PCM.ipynb - Delta modulation
Codage modulation delta.ipynb - Huffman coding
Codage Huffman.ipynb - Golomb–Rice coding
Codage Golomb-Rice.ipynb - Arithmetic coding
Codage arithmetique.ipynb - LZ77 coding
Codage LZ77.ipynb - LZW coding
Codage LZW.ipynb - Byte-pair coding
Codage par paires d'octets.ipynb - Run-length coding
Codage par plage.ipynb - Predictive image coding
Codage predictif sur image.ipynb - Mu-law effect
Effet de la loi mu.ipynb
Vision and video
- SIFT descriptors
Descripteurs SIFT.ipynb - Difference of Gaussians
Differences de Gaussiennes.ipynb - Mean-shift tracking
Suivi mean-shift.ipynb - Cross-correlation
Utilisation de la correlation croisee.ipynb - Mask R-CNN
Mask_RCNN.ipynb - Crossfade
Fondu enchaine.ipynb - Image overlay
Incrustation.ipynb
Other components
- Learner
Connections 32
- Learner → Acoustic features: explores
- Learner → Spectrograms: explores
- Learner → Peak detection: explores
- Learner → Image gradients: explores
- Learner → Matrix gradients: explores
- Learner → Image frequencies: explores
- Learner → Image surface: explores
- Learner → Image quality: explores
- Learner → DCT and quantization: explores
- Learner → KL transform: explores
- Learner → Wavelet transform: explores
- Learner → Deblocking filter: explores
- Learner → Floyd–Steinberg image: explores
- Learner → Floyd–Steinberg matrix: explores
- Learner → PCM coding: explores
- Learner → Delta modulation: explores
- Learner → Huffman coding: explores
- Learner → Golomb–Rice coding: explores
- Learner → Arithmetic coding: explores
- Learner → LZ77 coding: explores
- Learner → LZW coding: explores
- Learner → Byte-pair coding: explores
- Learner → Run-length coding: explores
- Learner → Predictive image coding: explores
- Learner → Mu-law effect: explores
- Learner → SIFT descriptors: explores
- Learner → Difference of Gaussians: explores
- Learner → Mean-shift tracking: explores
- Learner → Cross-correlation: explores
- Learner → Mask R-CNN: explores
- Learner → Crossfade: explores
- Learner → Image overlay: explores