GPU Accelerated JPEG Rendering
Nathan Egge
http://linux.conf.au/schedule/present...
GPU Accelerated JPEG Rendering
JPEG is the de facto standard for natural image compression on the web. Most
web browsers use the open source libjpeg-turbo for decoding JPEG files because
a) it is libre software and b) there has been significant investment in SIMD
optimization. However, in applications where the resulting image is immediately
uploaded to the GPU for compositing (like in modern web browsers) this approach
has two drawbacks:
1. The full uncompressed image must be uploaded to the GPU
2. Time spent decoding on the CPU robs computation from other tasks
These issues are exacerbated as more content is provided in high definition.
The jpeg_gpu project aims to solve both these problems by shifting the decode
computation as early as possible to the GPU. Partially decoded JPEG data is
uploaded as soon as the entropy coded symbols are read, and a set of pre-defined
shaders complete the rest of the decode into a GPU texture. Depending on the
resolution of the image and how much it is compressed, the speed-up can be
significant.
This talk will cover how the jpeg_gpu library is designed to take maximum
advantage of GPU parallelism. I will include a brief discussion of some
trade-offs made when designing the shader routines based on experimentation.
The real world performance of this approach will be discussed based on the
integration of jpeg_gpu into the WebRenderer compositing engine in Servo.