graph LR
Data_Management_Preprocessing["Data Management & Preprocessing"]
Core_Compression_Models["Core Compression Models"]
Entropy_Latent_Codec["Entropy & Latent Codec"]
Training_Optimization_Engine["Training & Optimization Engine"]
Evaluation_Benchmarking_Suite["Evaluation & Benchmarking Suite"]
Model_Zoo_Codec_Utilities["Model Zoo & Codec Utilities"]
Data_Management_Preprocessing -- "provides preprocessed input data to" --> Core_Compression_Models
Data_Management_Preprocessing -- "supplies data for evaluation to" --> Evaluation_Benchmarking_Suite
Data_Management_Preprocessing -- "handles data I/O for" --> Model_Zoo_Codec_Utilities
Core_Compression_Models -- "generates latent representations for" --> Entropy_Latent_Codec
Entropy_Latent_Codec -- "processes latent bitstreams for" --> Core_Compression_Models
Training_Optimization_Engine -- "optimizes parameters of" --> Core_Compression_Models
Training_Optimization_Engine -- "optimizes parameters of" --> Entropy_Latent_Codec
Evaluation_Benchmarking_Suite -- "evaluates the performance of" --> Core_Compression_Models
Model_Zoo_Codec_Utilities -- "provides pre-trained instances of" --> Core_Compression_Models
Model_Zoo_Codec_Utilities -- "integrates with" --> Evaluation_Benchmarking_Suite
click Data_Management_Preprocessing href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Data_Management_Preprocessing.md" "Details"
click Core_Compression_Models href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Core_Compression_Models.md" "Details"
click Entropy_Latent_Codec href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Entropy_Latent_Codec.md" "Details"
click Training_Optimization_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Training_Optimization_Engine.md" "Details"
click Evaluation_Benchmarking_Suite href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Evaluation_Benchmarking_Suite.md" "Details"
click Model_Zoo_Codec_Utilities href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Model_Zoo_Codec_Utilities.md" "Details"
The CompressAI architecture is designed as a modular ML toolkit for data compression, centered around a clear data processing pipeline. Data originates from the Data Management & Preprocessing component, which feeds into the Core Compression Models for neural network-based compression. The resulting latent representations are then handled by the Entropy & Latent Codec for efficient bitstream management. Model development is facilitated by the Training & Optimization Engine, which iteratively refines both the core models and the codec. Post-development, the Evaluation & Benchmarking Suite rigorously assesses model performance. Finally, the Model Zoo & Codec Utilities provides a user-facing interface for leveraging pre-trained models and performing encoding/decoding operations. This structure ensures a clear separation of concerns, enabling extensibility and efficient development within the deep learning compression domain.
Data Management & Preprocessing [Expand]
Responsible for loading, caching, and initial preprocessing of diverse data types (images, videos, point clouds), ensuring data is in the correct format for model consumption.
Related Classes/Methods:
Core Compression Models [Expand]
Contains the neural network architectures that perform the actual compression and decompression, transforming raw data into latent representations and vice-versa.
Related Classes/Methods:
Entropy & Latent Codec [Expand]
Manages the quantization and entropy coding/decoding of latent representations, crucial for efficient bit-rate reduction and decodability.
Related Classes/Methods:
Training & Optimization Engine [Expand]
Provides the comprehensive framework for training compression models, including defining loss functions, configuring optimizers, and managing training loops and schedules.
Related Classes/Methods:
Evaluation & Benchmarking Suite [Expand]
Offers a robust set of tools for evaluating the performance of trained compression models, including generating rate-distortion curves, calculating metrics like PSNR, and benchmarking against established codecs.
Related Classes/Methods:
Model Zoo & Codec Utilities [Expand]
Provides convenient access to a collection of pre-trained compression models, allowing users to quickly utilize state-of-the-art codecs. It also includes general utilities for encoding and decoding data using these pre-trained models.
Related Classes/Methods: