Skip to content

Core_Compression_Models.md

Latest commit

 

History

History
74 lines (43 loc) · 5.03 KB

Core_Compression_Models.md

File metadata and controls

74 lines (43 loc) · 5.03 KB
graph LR
    Compression_Models["Compression Models"]
    Core_Neural_Network_Layers["Core Neural Network Layers"]
    Specialized_Point_Cloud_Layers["Specialized Point Cloud Layers"]
    Entropy_Models["Entropy Models"]
    Latent_Codecs["Latent Codecs"]
    Compression_Models -- "composes and utilizes" --> Core_Neural_Network_Layers
    Compression_Models -- "orchestrates entropy coding with" --> Entropy_Models
    Compression_Models -- "configures and utilizes strategies from" --> Latent_Codecs
    Compression_Models -- "integrates and orchestrates layers from" --> Specialized_Point_Cloud_Layers
    Entropy_Models -- "relies on" --> Latent_Codecs
    Latent_Codecs -- "provides distributions to" --> Entropy_Models
    click Compression_Models href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CompressAI/Compression_Models.md" "Details"
Loading

CodeBoardingDemoContact

Details

The Core Compression Models subsystem is central to CompressAI, encapsulating the neural network architectures and their foundational building blocks for data compression and decompression. It focuses on transforming raw data into efficient latent representations and vice-versa, leveraging specialized layers, entropy coding, and latent code manipulation.

Compression Models [Expand]

These components represent the complete neural network architectures for specific compression/decompression tasks (e.g., image, video, point cloud). They orchestrate the encoder, decoder, and hyperprior networks to transform raw data into latent representations and vice-versa. This component acts as the high-level orchestrator for the compression pipeline.

Related Classes/Methods:

Core Neural Network Layers

Provides fundamental, reusable neural network building blocks (e.g., convolutional layers, residual units) that are common across various compression models. These are the basic architectural primitives upon which complex models are built.

Related Classes/Methods:

Specialized Point Cloud Layers

Implements specialized layers and operations tailored for point cloud data processing within compression models, such as PointNet++ operations or HRTZXF2022 specific layers. These layers address the unique challenges of 3D data compression.

Related Classes/Methods:

Entropy Models

Handles the entropy coding aspects of compression, including quantization of latent representations and estimation of their likelihoods (probability distributions). This component is critical for achieving high compression ratios by efficiently encoding the latent data.

Related Classes/Methods:

Latent Codecs

Provides mechanisms for manipulating, transforming, and potentially context-modeling latent codes generated by the encoders before entropy coding. It also provides probability distributions to the Entropy Models, enabling adaptive coding.

Related Classes/Methods:

FAQ