Modern video compression algorithms put significant strain on a system's CPU, especially for video encoding. The ever increasing demands for using video compression algorithms in a wide range of applications necessitate the use of processing components that boost the speed and quality of the video compression algorithm's execution. The vast parallel computational capabilities of modern graphics processing units (GPUs) that usually remain underutilized makes them suitable for handling the processing load for video coding. This paper examines and evaluates the performance benefits of using the GPU over the CPU for an experimental video compression algorithm. An NVIDIA CUDA GPU implementation is evaluated against a traditional multithreaded CPU implementation. Experimental results show that at the highest resolution examined, the GPU approach achieved an impressive speedup ratio of 21.303x against the CPU for the decoding process, while for encoding, the speedup ratio reached 11.048x. Overall results indicate the prevalence of the GPU over the CPU, justified reasonably by the massive parallelism offered by the GPGPU computing paradigm, showing that the GPU should be the architecture of choice for high definition video coding.
|Title of host publication||2015 Seventh International Workshop on Quality of Multimedia Experience (QOMEX)|
|Publication status||Published - 2015|
|Name||International Workshop on Quality of Multimedia Experience|
- video compression
- contourlet transform
Katsigiannis, S., Dimitsas, V., & Maroulis, D. (2015). A GPU vs CPU performance evaluation of an experimental video compression algorithm. In 2015 Seventh International Workshop on Quality of Multimedia Experience (QOMEX) (International Workshop on Quality of Multimedia Experience). IEEE. https://doi.org/10.1109/QoMEX.2015.7148134