A closer look at JPEG performance

In my previous post, I compared JPEG to the newcomers, WebP and JPEG XR. For the most part, JPEG held its own pretty well. WebP was better in a few respects, worse in a few...overall decent. JPEG XR was utterly horrible.

But this got me thinking: how optimized was my JPEG encoder? I first used MSFT's .NET JPEG implementation, but I had to wonder just how good that implementation actually was. For example,

1. What quantization tables does it use?
2. Are its quantization tables computed on a per-image basis, or is it using the IJG reference tables?
3. How are the tables being scaled, and are they being capped at 8 bits (i.e. no value larger than 256 in your quantization table) or allowed to go to 16?
4. For the discrete cosine transform, is it using integer or floating point math?
5. Is it using an optimized entropy encode?

Maybe our problem isn't JPEG. Maybe our problem is bad JPEG encoders. For example, libjpeg certainly isn't terrible, but there are some flaws that are hard to overlook, such as hard-coded quantization tables. The linear scaling of the quantization matrix is equally deficient, and this was acknowledged by the maintainers of the project ("The current method for scaling the quantization tables is known not to be very good at low Q values," taken from the libjpeg README in February of 1996! How's that for old?). The library itself still defaults to non-optimal settings (probably a perfectly legitimate design decision when you weren't sure if you had an FPU...these days, it's a much safer bet that you do). In summary, there's some serious room for improvement.

So do we really need WebP? Or do we really just need to give a venerable old method some much needed love?

It is clear to me that there are various methods that can improve current JPEG performance. Using libjpeg's optimize routine, followed by a run through jpegcrush, WebP is no longer ahead. Jpegcrush itself "...abuses combinations of multiple Huffman tables and scan combinations to improve compression more than merely optimizing the Huffman tables naively," according to Dark Shikari. There are several papers about generating image specific quantization tables that minimize error for a given bitrate that look promising. So in my spare time, I'll probably investigate some of this stuff further.