3 repository-uri
Image optimization techniques that remove metadata and optimize encoding without discarding pixel data.
Distinct from Image Optimization Tools: Focuses specifically on the lossless aspect of image optimization, distinct from general web compression
Explore 3 awesome GitHub repositories matching web development · Lossless Optimization. Refine with filters or upvote what's useful.
ImageOptim is a macOS image optimizer and GUI image compressor designed to reduce image file sizes for web and disk storage. It functions as a lossless image optimizer that removes unnecessary metadata and optimizes encoding to reduce file sizes without losing pixel data. The application handles macOS media asset management and web image preparation by shrinking files to improve website loading speeds and reduce storage usage. It provides both lossless and lossy compression options to maintain visual quality while reducing the disk space used by images.
Optimizes image encoding and removes metadata to reduce size without any loss of visual quality.
QOI is a lossless image codec and encoding standard designed for high-speed compression and decompression of raw pixel data. It provides a toolkit for translating raw image buffers into a compact format and back into pixel representations without any loss of quality. The implementation focuses on fast image encoding and decoding, enabling the rapid conversion of compressed image data back into raw pixels. It also supports image format conversion to ensure compatibility across different software systems and hardware.
Recovers exact original color values without approximation or quantization.
Mozjpeg is a high-performance C library for encoding, decoding, and transcoding JPEG images. It serves as a binary-compatible, drop-in replacement for standard JPEG libraries, maintaining existing function signatures to improve compression efficiency without requiring changes to application logic. The library functions as an image optimizer that reduces file sizes through lossless progressive encoding and coefficient optimization. It utilizes trellis-based quantization and SIMD-accelerated processing to optimize the trade-off between visual quality and file size. Its broader capabilities inc
Optimizes existing JPEG files using progressive encoding and rescan techniques to reduce size without losing image data.