"Blueberry" H1 VP8 Hardware Encoder IP Released

"Blueberry," the second release of the H1 VP8 hardware encoder, is now available through the WebM Project hardware page. Due to the short growing season and abundant light during the summer, Nordic blueberries are exceptionally sweet and rich with vitamins. The Blueberry encoder is not too bad either!"In Blueberry we focused primarily on improving the […]

"Blueberry," the second release of the H1 VP8 hardware encoder, is now available through the WebM Project hardware page. Due to the short growing season and abundant light during the summer, Nordic blueberries are exceptionally sweet and rich with vitamins. The Blueberry encoder is not too bad either!

"In Blueberry we focused primarily on improving the encoder for video calling use, as many of the semiconductor companies that have licensed the H1 encoder plan to use it in these use cases. Compared to Anthill, the average measured PSNR improvement was 0.82 dB, while SSIM figures were improved by 0.011. This is also shown in the following chart for 720p video call content, where Blueberry achieves the same quality as Anthill with 25% less bits!, Google explained.

"The following text assumes the reader has prior knowledge about video codecs and hardware designs.

We reached the aforementioned +0.82 dB PSNR gains by adding the following features to the encoder:

  • Improved encoding decisions and added more coding options at macroblock level
  • Enabled multiple motion vectors per macroblock (Split MV mode)
  • Added preference of "nearest", "near" and "zero" type macroblocks that're less expensive to code than others
  • Added support for up to two reference frames in motion search (immediately previous and Golden frame)
  • Added deblocking filter macroblock mode adaptivity support
  • Added ¼ pixel precision motion estimation at 1080p resolution (previously supported only up to 720p)
  • Increased the amount of token probability tracking counters (enables more efficient entropy coding)

In addition, we added support for a programmable segment map, which enables psychovisual quality optimizations and defining region-of-interests. This means we can for example code the foreground objects (i.e. people) with a better quality (smaller quantizer) than the static background. We also added new hooks to the hardware that allows us to improve the quality of the encoder by later firmware upgrades that optmize our cost function algorithms - even after the chip has been manufactured," Google stated.

For more technical details read here.

[Source:WebM blog]