oavif: Faster target quality image compression

Summary

oavif is a new approach to target quality encoding in image compression, designed around smarter convergence strategies and quicker scoring to be as fast as possible. Why? Target quality encoding is one of the highest impact use cases for image compression. A target quality encoder framework aims to produce an image encoded at a particular quality set by the user according to some metric or visual quality index. This kind of encoder framework is useful for a variety of users, ranging from small website owners to content delivery networks pushing vast quantities of image data through the Web. The value of target quality encoding is perceptual consistency. If I rely entirely on my encoder's internal quality index (often set with a "q" parameter), I may not get outputs of perfectly consistent quality when using the same "q" across different images. Relying on a metric that represents the viewer's experience is the solution to this; targeting a representative score within this metric will ensure you always receive an optimally encoded file that never undershoots and ruins image quality, and never overshoots and wastes data. oavif is a tool to do target quality encoding extremely quickly. There are three core components to a target quality encoding framework: the metric, the encoder, and the convergence algorithm. oavif aims to leverage or improve the state of the art in all three categories. I think this use case has been neglected because it sits in an awkward spot, stuck between encoder development and content deployment. Considering image encoders and powerful metrics are fast, it is easy to take them for granted and build inefficient frameworks around them. Slow frameworks waste valuable resources; processing images is expensive. I built oavif with the same approach I've adopted when building encoders, where every CPU cycle counts. Metric There are good metrics and bad metrics in the context of what humans care about in images. PSNR is a bad metric; targeting a PSNR...