RE: Results of testing Codepoint Prediction for Patch/Subset

Thank you Garret, this is great info – let’s discuss it next Monday during the WG call.
Cheers,
Vlad


From: Garret Rieger <grieger@google.com>
Sent: Tuesday, July 21, 2020 6:54 PM
To: w3c-webfonts-wg (public-webfonts-wg@w3.org) <public-webfonts-wg@w3.org>
Subject: Results of testing Codepoint Prediction for Patch/Subset

I've gotten the complete results for adding codepoint prediction to the patch/subset transfer methods and re-running the simulations. You can find a detailed writeup of the results here: https://docs.google.com/document/d/1u-05ztF9MqftHbMKB_KiqeUhZKiXNFE4TRSUWFAPXsk/edit?usp=sharing<https://protect-us.mimecast.com/s/qPViCER2v0u35zX2HNNk5k>

Executive summary from the doc:

"Codepoint prediction, that is adding extra non-requested codepoints to a font augmentation response was tested with the progressive font enrichment simulations to see if it could improve performance and close the gaps where progressive font enrichment performed worse than existing transfer methods.

The result of the simulations showed that for all three script categories using codepoint prediction was able to lower costs enough to tie or beat existing transfer methods for all connection types other than Mobile 2G and the slowest variant of Mobile 3G. This comes at the cost of sending more bytes, but still sends less bytes than existing transfer methods.

CJK scripts benefited the least from prediction. Arabic, Indic, Latin, Cyrllic, Greek, and Thai saw more improvements.  For those scripts prediction closed the performance gap between existing font transfer methods for many connection types.

Prediction was not beneficial in all cases. For some script and network conditions combinations it increased overall cost. If prediction is to be used it should be selectively enabled based on the script and client network capabilities.

Lastly the simulations demonstrated that comparing the total cost of incremental optimal vs one font optimal method can help point us to where using prediction will likely lead to improvements in cost. The one font optimal method provides a lower bound for the performance of a perfect prediction algorithm."