In the future, we plan to support additional input and output image formats, including emerging image formats such as AVIF and JPEG XL. This enables us to create the most compact representation of the image that is possible under the JPEG standard.Īs you probably know, JPEGmini is not limited to JPEG images: In JPEGmini 3.0 and beyond, we use a similar technique to create an optimized JPEG from a source HEIC image. This set of parameters determine the amount of compression applied to each individual 8×8 pixel block, and we modify this set adaptively, based on the amount of additional compression we wish to apply, and on the characteristics of the source JPEG image. We actually go much deeper into the JPEG file, and modify a whole set of parameters that is called the “Quantization Matrix”. It’s important to note that the “encoding parameters” we use to create the candidate and final JPEG images are not simply an indication of a single “Quality Factor” (QF) number. The process is described in the diagram below. This is the selected candidate that JPEGmini outputs as the final optimized JPEG. Finally, we determine which is the smallest candidate (in bytes) that does not exhibit any additional visible artifacts when compared to the original JPEG. Then we use BQM to compare the quality of the candidate images to the source image. Basically, we take the original JPEG image and apply higher levels of compression to produce several candidate images that are more compressed than the original JPEG. JPEGmini relies on BQM in its optimization process. More information about BQM can be found in Beamr’s blog post and podcast episode. The tile quality score is then used to control the amount of compression applied to that tile. These different attributes are analyzed for each of the image “tiles”, which are small segments of the image, and combined into a single “quality score” for that tile. BQM looks at various aspects of the difference between the two images: How the pixel values differ between the input and the optimized image, how much blockiness was added, how has the texture changed, etc. So BQM can tell us with a very high level of accuracy, whether any visible artifacts exist in our optimized images when compared to the original images. This is information that is imperceptible by humans, and there is no point in keeping information that we can’t see… But how do we identify such information, and remove it, while making sure all “essential” information is preserved? This is where the Beamr Quality Measure (BQM) comes into play.īQM is a unique quality metric – it’s a mathematical algorithm, which is highly correlated with the way humans perceive image quality. How Does JPEGmini Work For Photos?īasically, the way JPEGmini performs its “magic” of reducing file size without affecting quality, is by removing perceptually redundant information from the image file. After you understand how it works for images, it will be much easier to extend this concept to video optimization. But before we do that, let’s take a look at the image optimization technology first, as this is the basic building block of the technology. In this blog post, we’ll explain how the new video optimization feature works under the hood, and what you can expect when using it on your videos. The technology behind the image and video optimization solutions developed by Beamr is very unique: The algorithms are covered by 34 granted international patents, and we were recently awarded the 2021 Technology and Engineering Emmy® award from the National Academy of Television Arts & Sciences for this technology. Sapon on 5 Reasons Why BPG Will Eventually Replace JPEG.
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