The case against JPEG XL

Investigating JPEG XL's place as a Web image codec. Why? JPEG XL is a technically impressive image codec; it is a definitive upgrade over JPEG, more versatile than WebP, and well-equipped to serve use cases beyond the Web. However, it was famously rejected from Chrome in 2023. Because this happened to a royalty-free, flexible, compression-efficient codec from the JPEG Committee that was receiving attention from large companies, the decision didn't land well with many. Recently, a JPEG XL decoder in Rust has made its way into Firefox and Chrome in some capacity. The Web's major stakeholders may therefore be reversing course on JPEG XL given that the new decoder may protect the Web from reliving 2023's WebP vulnerability. Is this all it took to justify JPEG XL for the Web? Historically, I've been a big proponent of JPEG XL for all use cases. I endorsed JPEG XL for Interop 2024, and I've interacted with Jon Sneyers and Jyrki Alakuijala (two of the format's primary authors) personally many times. I'm consistently impressed with their public conduct, level-headedness, technical aptitude, and passion for the field. This piece does not seek to discredit the format's authors or their work, nor to claim any political affiliation relative to the codec's symbolism in free software. The spirit of this post is educational; I want to offer an empirical look at the current state of image compression and the Web platform in 2026. Some inspiration is drawn from RISC-V: They Should Have Known Better by Dmitry Grinberg. The Web I do image compression work, coming from video compression originally. While working on an AV1 encoder, Julio Barba and I made significant advancements to AVIF, and I learned a lot in the process. When I decided to start building my own encoder, I had to think very hard about which formats I felt had the highest ceilings, could be effectively optimized, and had the most present and potential utility. I decided not to work with JPEG XL. By volume, there are very few use cases on the Web that aren't served by versatile lossy compression. The average Web consumer doesn't need lossless; they just need a lossy codec versatile enough to prevent terrible artifacts (e.g. JPEG on non-photographic content). This rules out JPEG XL's lossless advantage, which in practice is only roughly 11.9% smaller than lossless WebP anyway - and on an unrealistic test dataset for the Web (157 MP photos, 10 MP illustrations, and 27 MP books). It cannot be worth bringing a new image codec to browsers to save 12% on a tiny volume of image content with use cases inherently less sensitive to bandwidth constraints. I say this because JPEG XL isn't competitive for lossy, so lossless would be its only real advantage. Lossy Compression Efficiency One of the original arguments for JPEG XL was that its reference encoder was more perceptually optimized than competing encoders. Now, on both speed and fidelity per bit, other encoders are stronger. The AV1 reference encoder received specialized perceptual tuning based on controlled subjective human trials to strengthen its efficiency while maintaining a tuning mode optimized for perceptual metrics. SVT-AV1 has similar tuning modes. There is no compelling argument that modern encoders aren't tuned for the human eye. Metrics aren't perfect, but they paint a daunting picture for JPEG XL: aperture-alpha is Halide Compression's upcoming encoder, codenamed Aperture. I included it to show just how much ground libjxl needs to make up to compete at the frontier. Some analysis claims that JPEG XL underperforms in metrics relative to its perceptual strength, but I don't see sufficient evidence that this is to the degree that graphs like the ones I shared could be secretly completely reversed. CVVDP and SSIMULACRA2 are very strong perceptual metrics, and definitely tell us something when the differences are this great. For AVIF, libaom's perceptually optimized tune (tune IQ) is only a couple of points lower than its perceptual-metric-optimized tune (tune SSIMULACRA2). Plus, the JPEG XL reference encoder has historically suffered from percep tual issues that remain largely unresolved. There's no such thing as a codec benchmark, only an encoder benchmark; in theory, the ceiling for JPEG XL as a format is higher than libjxl is getting. But how hard would it be to close the gap? As a compression engineer, I believe it is disadvantaged here. Some reasons: - JPEG XL doesn't have directional prediction modes. Compressed images are divided into VarDCT blocks (from 2x2 up to 256x256) and transformed into frequency representations of their pixels. Other block-based image codecs like WebP let you predict a block's pixels using surrounding data, subtract this prediction from the actual pixels, and then do the frequency transform. Directional prediction modes can result in blur if your encoder isn't perceptually optimized, but strong mode-decision pipelines can pick the right mode for the job and save lots of bits. For example, edge preservation is stronger in codecs with directional pred, while JXL is weaker here. - The proposed solution for the edge-preservation gap is splines, which are vastly more difficult to use. The hard part is on the encoder side: you need an efficient algorithm to figure out which pixels can even be represented as a spline, then feed every candidate through RDO to decide whether it's worth coding. There's no existing PoC for using splines for edge preservation, and I have no reason to believe they'd be better than dir-pred anyway. - JPEG XL doesn't have deblocking loop filtering (DLF), or any deblocking filter. It does have two in-loop tools that are sometimes offered as partial equivalents: gaborish, which is the closest thing JXL has to AV1's loop restoration filtering, and EPF (edge-preserving filter), whose closest analogue is AV1's CDEF. Neither is a deblocking filter, and the two together can't fully replace proper DLF. The DLF can smooth images out, but if your encoder is smart it will only help you avoid mosquito noise, which JPEG XL still suffers from. - JPEG XL's perceptual "XYB" colorspace is based on a lot of intuition, and doesn't always translate to gains in other formats (like JPEG) even when metrics like SSIMULACRA2 work in the exact same colorspace. The claimed efficiency savings from using XYB also aren't as big as originally advertised because libjxl currently relies on aggressively quantizing the B channel. This has resulted in subpar color preservation, which new JXL encoder developers must explicitly undo. - JXL does poorly with non-photographic images. The proposed solution is using patches, but they are more difficult to use than AV1's Intra Block Copy. - To get a similar range of expressiveness to IntraBC, the encoder has to deal with additional concepts like layers and blending, which aren't cheap to represent at the bitstream level. - Residual coding is awkward. With AV1, you predict a block, subtract the prediction from the source, and the transform coefficients naturally represent the residual. With JXL's construction, you decode a residual frame and then blend a reference patch, so you need an actual frame or layer whose decoded pixels represent the residual. That would likely be a Modular frame, which is interesting because Modular isn't restricted to conventional unsigned image values the way the final rendered image is. - An IntraBC block essentially costs a motion vector plus residual coefficients, whereas a JXL construction potentially costs a reference frame, a frame header, a crop, blend information, a patch dictionary entry, patch coordinates, and a residual frame. That overhead can overwhelm the savings unless the repeated region is fairly large or reused many times. - Patches have to be explicitly enabled in libjxl below effort 7 because they currently have performance issues. For non-photographic images, the argument that “they should be vector images” doesn't hold up because many images could be vector images but aren't, and they can't be vectorized perfectly. “The world should be different” is not a justifiable defense against optimizing for the way the world actually is. It is tempting to think these points mean the ceiling is higher than libjxl lets us reach and that we could do better, but I'm not confident it can eclipse well-optimized AVIF encoders quickly, given its less intuitive (and potentially weaker) coding tools. Decode Time JPEG XL has an impressively flexible specification. In addition to its coding tools, it supports up to 4096 channels, arbitrary color depth, progressive decode, JPEG recompression, and more. Many of these features are not broadly useful on the Web; you need 4 channels (RGB/YUV + alpha), reasonable color depth to support HDR (10-bit is fine), and the ability to load quickly. Progressive rendering (which AVIF supports) decodes a low-fidelity rendition before the full image arrives. AVIF didn't support progressive rendering for a while, and during that time I believe it was deeply oversold. Now that libavif has implemented it (it was always possible), the conversation appears to be over. I think this is because the results speak for themselves: This is from the JPEG-XL info site, where AVIF shows a usable image much earlier than JXL at just ~2-3% of the full image's size. Combined with the fact that the AVIF is smaller overall, this is an easy win. I've screenshotted the page because the AVIF progressive decode only works in Chrome, as it is using the browser's native decoder; JPEG XL uses a polyfill because even in Safari where it is supported, progressive decode isn't. JPEG recompression is the ability to losslessly re-encode JPEGs as JXL images while saving bits; the oft-cited number is 20% savings. However, the user pays for this in decode time, as recompressed JPEGs take ~33% longer to decode. Modern consumer devices are powerful, but the argument that the savings come “for free” is misleading. On that topic, decode