Name
Abstract | ||
|---|---|---|
The performance of many applications, such as video streaming, webcam conferencing, and aerial surveillance, all greatly depend on video quality. A major issue with higher quality video is that either more data bandwidth or storage resources must be dedicated for transferring or storing the video. However, if the low-resolution video is transferred or stored in order to conserve data bandwidth and storage space, super-resolution is a viable solution that can be applied afterwards on the receiving end to rectify the poor quality of the low-resolution video. Super-resolution is an imaging technique that leverages motion blur and multiple low-resolution frames to construct a high-resolution frame. In our paper, we implement and analyze a super-resolution algorithm across multiple platforms ranging from purely hardware to purely software and even a mix of both hardware and software. More specifically, we examine the performance for a field-programmable gate array (FPGA) implementation on two different FPGAs, a software/hardware solution on a FPGA with a soft core processor, a general purpose graphics processing unit (GPGPU) implementation, and a MATLAB implementation. Overall, we found that the GPGPU provides the best overall performance with up to 29 FPS with 35 iterations of the super-resolution algorithm. A high-performance FPGA can have comparable performance and rival the GPGPU in some cases. One of the interesting results was that the hardware/software FPGA combination performed worse than the pure software implementation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1687-6180-2013-116 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
Super-resolution algorithm, Video processing, High-resolution frame, Streaming video, Field programmable gate arrays, Graphics processor | Computer science,Software,Artificial intelligence,Computer hardware,Video quality,Computer vision,Video processing,Field-programmable gate array,Video tracking,Bandwidth (signal processing),General-purpose computing on graphics processing units,Graphics processing unit,Embedded system | Journal |
Volume | Issue | ISSN |
2013 | 1 | 1687-6180 |
Citations | PageRank | References |
5 | 0.39 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Leung | 1 | 5 | 0.39 |
| Seda Öǧrenci Memik | 2 | 488 | 42.57 |