GoKawiilBuilding the Next Generation Image Standard
The internet runs on images. Since the early days of the web, there has been a relentless tension between visual fidelity and bandwidth. For decades, the industry relied on the venerable JPEG standard for images loading fast. It served us remarkably well, but as displays moved to High Dynamic Range (HDR) and Wide Color Gamut (WCG), the format began to show its limits.
The road to JPEG XL (JXL) wasn't a straight line. It was a decade-long exploration, creating a series of milestone projects testing radical ideas in psychovisual modeling, entropy coding, and optimization. Today, as JPEG XL sees rapid adoption across operating systems and professional standards, we’re looking back at the experiments that made it possible.
The Early Foundation: 2011–2017
Our study began with a focus on understanding the limits of existing technology. We didn't start by trying to write a new standard; we started by trying to make the current ones better, and learning their limitations. This allowed us to make the new formalism more flexible and efficient in the right places.
WebP Lossless and Brotli: Lossy WebP drew its lineage from video technology, the WebP Lossless (2011) represented an architectural and scoping departure. We debuted the entropy image concept , an innovative method utilizing a secondary image to orchestrate the selection of static entropy codes for the primary visual data. We reapplied this approach later with data-driven context modeling in the Brotli compression format , enabling rich context modeling without slowing decoding.
Lossy WebP drew its lineage from video technology, the (2011) represented an architectural and scoping departure. We debuted the , an innovative method utilizing a secondary image to orchestrate the selection of static entropy codes for the primary visual data. We reapplied this approach later with data-driven context modeling in the , enabling rich context modeling without slowing decoding. Butteraugli: Around 2014, we realized that raw mathematical compression (PSNR) wasn't enough, and simple psychovisual approximations (SSIM and similar) failed in color-rich environments. We built Butteraugli and the XYB color space to mimic the human visual system's edge detection and opponent-color processes in varying scale, allowing us to compress images more effectively.
Around 2014, we realized that raw mathematical compression (PSNR) wasn't enough, and simple psychovisual approximations (SSIM and similar) failed in color-rich environments. We built and the to mimic the human visual system's edge detection and opponent-color processes in varying scale, allowing us to compress images more effectively. We pushed the legacy JPEG 1 standard (ISO/IEC 10918, introduced in 1992) to its absolute limits through two key projects: Guetzli and Brunsli. These initiatives provided invaluable insights into the strengths and limitations of traditional JPEG compression methods. Guetzli (2016) is a slow high-density perceptual encoder that used Butteraugli to find the optimal quantization tables, pushing legacy JPEGs to be 20-30% smaller. Brunsli (2015) meanwhile, focuses on lossless recompression, allowing users to repack existing JPEGs into a smaller footprint without losing a single bit of original data. After finishing with JPEG XL standardization, we returned to Guetzli's scope in 2024 and made the encoding much faster and HDR-compatible in Jpegli.
The feedback from these launches, ranging from the technical details of WebP Lossless to the psychovisual audits of Guetzli, proved indispensable. While we already targeted the highest visual fidelity, feedback from detail-critical e-commerce helped us to refine the requirements.
The Convergence: 2017–2019 PIK Era and the 2019 FUIF Integration
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