{"categories":[{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"}],"enrichment":{"capability":"A Python wrapper for the QOI (Quite OK Image) lossless image format that encodes and decodes images as numpy arrays, offering faster compression and decompression than PNG with comparable file sizes.","skillfed_tags":["image-codec","numpy-native","fast-compression"],"use_cases":["Real-time image capture and storage in computer-vision applications where encoding speed matters more than compression ratio.","Batch processing large image datasets where lossless fidelity is required but PNG is too slow.","High-throughput image pipelines using thread pools to encode or decode many images concurrently.","Lossy image compression for scenarios where visual quality (SSIM ~0.94) is acceptable and speed is critical.","Intermediate image format in machine-learning training loops where numpy arrays are already in memory."],"what_it_does":"QOI is a Python wrapper around the Quite OK Image format, a lossless image codec designed for speed. It reads and writes images as numpy arrays, making it a natural fit for computer-vision and scientific-computing workflows. The package depends only on numpy and compiles to platform-specific wheels, avoiding build friction on most systems.\n\nThe format trades some compression efficiency for encoding and decoding speed: lossless QOI is typically 4\u201320x faster to encode and 1.5\u20136x faster to decode than PNG, though file sizes are slightly larger. The package also supports a lossy mode via downscaling, which can achieve JPEG-like compression ratios at 5\u201310x faster encode and 7\u20138x faster decode speeds. Multi-threaded encoding and decoding are supported without GIL contention, making it suitable for batch processing and high-throughput image pipelines.","worth_installing":"Yes, if you work with numpy arrays and need faster lossless image I/O than PNG. The package is stable, well-maintained, has no security issues, and installs easily on modern Python. Skip it if compression ratio is your primary concern or if you need formats other than lossless/lossy RGB."},"id":"qoi","links":{"html":"https://skillfed.io/packages/qoi","md":"https://skillfed.io/packages/qoi.md","pypi":"https://pypi.org/project/qoi/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-01-27","license_spdx":null,"license_treatment":"permissive","name":"qoi","python_support":"supports_current","summary":"A simpler wrapper around qoi (https://github.com/phoboslab/qoi)"},"popularity":{"monthly_downloads":212048,"position":9468,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.2"}