{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"File Formats","url":"https://skillfed.io/packages/category/file-formats"},{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"}],"enrichment":{"capability":"Encodes and decodes image data using the JPEG-LS lossless compression algorithm via the CharLS C++ library, producing compressed byte buffers that can be round-tripped with numpy arrays.","skillfed_tags":["image-compression","lossless-codec","medical-imaging"],"use_cases":["Compress greyscale or RGB image data in memory for storage or transmission when lossless fidelity is required.","Integrate JPEG-LS compression into medical imaging pipelines where the standard is widely used.","Benchmark or compare JPEG-LS compression ratios and speed against other lossless formats in scientific research.","Reduce memory footprint of large image datasets by compressing numpy arrays before serialization or caching."],"what_it_does":"pyjpegls is a Python wrapper around the CharLS C++ library that implements the JPEG-LS lossless image compression standard (ISO-14495-1/ITU-T.87). It provides encode() and decode() functions to compress numpy arrays to byte buffers and decompress them back losslessly. The package was forked from the unmaintained CharPyLS project to maintain compatibility with modern Python versions and add fixes.\n\nThe typical workflow is to read image data into a numpy array, call encode() to compress it to a buffer, and call decode() to restore the original array bit-for-bit. JPEG-LS is designed for low-complexity lossless compression and is often used in medical imaging and scientific applications where data fidelity is critical. The package depends only on numpy at runtime; Cython and Pillow are build-time or test-time dependencies.","worth_installing":"Yes, if you need lossless JPEG-LS compression in Python and are comfortable with a package in aging maintenance. The library is stable (Production/Stable classifier), has no known vulnerabilities, and pre-built wheels make installation straightforward on common platforms. However, verify that the fork remains compatible with your use case and that the 655-day release gap does not signal abandonment; check the repository's recent activity and open issues before committing to a production dependency."},"id":"pyjpegls","links":{"html":"https://skillfed.io/packages/pyjpegls","md":"https://skillfed.io/packages/pyjpegls.md","pypi":"https://pypi.org/project/pyjpegls/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-10-28","license_spdx":null,"license_treatment":"permissive","name":"pyjpegls","python_support":"supports_current","summary":"JPEG-LS for Python via CharLS C++ Library"},"popularity":{"monthly_downloads":119065,"position":12091,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.1"}