SReC

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SReC is a Content & Design tool that frames lossless image compression as a super-resolution problem. It applies neural networks to compress images while maintaining high quality.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is SReC?

SReC is a PyTorch implementation of "Lossless Image Compression through Super-Resolution." This open-source tool leverages neural networks to achieve state-of-the-art compression rates on large datasets with practical runtimes. It supports full training, compression, and decompression functionalities, allowing users to convert images into highly compressed .srec files and then back to PNG without loss. SReC provides pre-trained models for datasets like ImageNet64 and Open Images, making it accessible for immediate use. The project is hosted on GitHub, offering a comprehensive solution for efficient and high-quality image data management.

Best used for

Ideal for developers and researchers who need to implement advanced lossless image compression, train custom models for specific datasets, and convert images to and from highly compressed formats. Especially valuable for those working with large image datasets requiring efficient storage and transmission.

Common actions

compress images

implement image compression

research image processing

open-sourceworkflowscollaborationdeepfakeface swapping"AI Agents"low-code/no-codeautomated workflowgithub copilot

Capabilities

Key features

Lossless image compression
Super-resolution framing
Neural network application
PyTorch implementation
Training support
Compression/decompression
Pre-trained models

Target Audience

ai/ml researchersdeep learning engineerscomputer vision developers

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming language and framework is SReC implemented in?

SReC is primarily implemented in Python, utilizing the PyTorch deep learning framework. It also includes components written in C++ and Cuda for performance-critical operations, making it suitable for high-efficiency image processing tasks.

Does SReC provide pre-trained models for immediate use?

Yes, SReC offers pre-trained models for popular datasets such as ImageNet64 and Open Images (PNG). These models allow users to quickly evaluate the tool's compression capabilities without needing to perform extensive training from scratch.

Can SReC be used for both compression and decompression?

Absolutely. SReC fully supports both compression (encoding) and decompression (decoding) of images. It converts standard image files into its proprietary .srec format for efficient storage and can then revert them back to original image formats like PNG.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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