Implementation of real time image processing on serial general purpose processors is hard to achieve. This is due to the limited resources, general purpose architecture and large data set presented by image. For example if we have to perform single operation on every pixel of the 640x480 gray scale frame from input video source at 30 frames per second, it will require the serial processor to perform 1.84 million operations per second excluding the operations required for reading from and writing data to buffers. This demands a very high throughput serial processor (GPP).
The above stated problem of image processing at real time can be countered by using FPGAs by using their inherent parallelism of hardware which is an advantage over the fetch decode architecture of processor. Along with solutions comes the complexity of implementation. The software algorithms developed for general purpose processors cannot be directly implemented on FPGA and need to be converted to take advantage of parallelism and meet the constraints implied by hardware. There are popular ways to implement software image processing codes in high level languages (HLL) to FPGA.
- Using HLL to HDL compilers
- Manual conversion
The cases where the hardware implementation is functionally equivalent to software implementation, the mapping is easy and compilers can be used efficiently. Sometimes standard algorithm made for software is not compatible for hardware due to reasons like, the implementation requires too many resources or accesses memory in a way which the hardware cannot support. In such cases we need to re write the algorithm in hardware keeping in mind the limitations and constrains of hardware.
The major constraints implied by hardware implementation are:
- Resource utilization and conflict
The affect of these constraints is closely dependent on the processing model adopted for implementation of the application. The general processing models used are:
- Hybrid processing
In stream processing the design samples incoming data like raster scan and perform as much operations as possible. In this mode the role of memory is very important and the processing speed is dependent on bandwidth of memory.
Offline processing doesn’t imply much constraints and it is most suitable for direct mapping of software based algorithm. Hybrid processing is mixture of stream and offline processing and the timing constraints re relaxed as the image is sampled at slower rate.