Prior to this PR we had two vec4 implementations for floats, the legacy "float4" and the newer "vfloat4". The newer vfloat4 is a superset of the capabilities of the float4, and using it everywhere would give other opportunities to optimize by e.g. using the conditional select functionality rather than scalarizing chunks of vector code.
This patch is just the basic migration - where possible I've translated the original code without additional refactoring. Further optimizations to make better use of the vfloat4 functionality will come later. Overall this patch has no impact on IQ or performance, but does give a slight reduction in code size.
This implementation alters the algorithm and vectorizes these two functions.
The algorithm change will have a minor image quality impact; it now uses a faster approximation for selecting which component to assign to a second plane when choosing a partition. There is an insignificant IQ hit on -medium (under < 0.01dB), as we rarely use two planes anyway, and a slightly larger one on -thorough (under < -0.05dB). The significant (>10%) performance gain on -thorough is worth the quality loss (i.e. users are more likely to use -thorough if it's faster, and the overall IQ gain vs medium is still net positive).
* Update test data reference CSV
* Formatting cleanups
* Make image data type flexible
- No new types supported yet, but it's a precursor to just supporting f32 as an interface type.
This phase introduces the first phase of the API split between the API front-end and the CODEC backed. However, some aspects of the final design are not yet supported:
This build only only supports automatic multi-threading; no user owned thread pools are supported.
Some memory allocations still tied to the compression pass, not the context, so reusing the context will have higher than planned CPU overhead due to the repeated memory allocation (although the impact is probably small unless compression small image).
The input image layout is still the legacy format and must be pre-padded by the caller for any pass using the area-based perceptual metrics.
pete