Hello, it's been a while since I went here. The reason is that I studied the oric video organisation and tried to adapt my algorithms to the oric constraints.
I've made numerous experiments and found an algorithm which, I think, works pretty well and produces really oric-compatible pictures this times Main features of the algorithm are:
- It uses Ostromoukhov's error-diffusion coefficients which reduces the worm-effects usually observed with error-diffusion technique.
- It uses an approximation of CIE's Delta-E 2000 to compare colors. Delta-E better represent color difference than euclidean distance in RGB color-space.
- The error is filtered according to the saturation of the original color. This allows saturated colors to stay "pure", reducing the noise, hence inducing less color-clashes and improving quality a lot. (This is original idea #1) This also can be used to make the piture being too artificial by abusing saturated colors.)
- Using harmless simplifications about propagation of effors between octets (idea #2), it capable of exploring the whole depth of the decision-tree for a line (pipi.exe only seem to explore depth 2 max). This ensure that the minimal delta-E is obtained for each row.
- It is fast. It takes around 20sec/image under grafX2 (interpreted LUA).
- It is simple and straightforward. There are very few black-magick tricks in there. It is just idea #1 and #2 working together.
Some images are good and some others are... well.. could be... better Actually the algorithm has a pair of parameters in error filtering that can improve the quality. I need more work to find one set of param that works best for all of my corpus. Anyway, the provided ones are quite good at the moment (but I know I can have even better quality).
And you, what do you think about these results?