How to implement a high-definition video design framework for FPGAs
April 06, 2008 -- pldesignline.com
Almost all new design starts for video/imaging systems – be it in broadcast, studio, medical, or military applications – is processing high-definition (HD) video signals. A frame of HD video has between 5 to 12 times the numbers of pixels as the frame of SD video as illustrated in Table 1.
Table 1. Frame sizes in pixels for different HD resolutions compared to standard definition (SD).
This increase in the number of pixels per frame directly translates into increased video processing throughput requirements that drive most of HD video system designs to FPGAs.
With inherently parallel DSP blocks, an abundance of embedded memory blocks, a large number of registers, and high speed memory interfaces, FPGAs are ideal for HD video system design. However, HD video signal processing on FPGAs also has significant challenges, such as implementing efficient external frame buffer interface, interfacing different video function blocks, integrating the signal processing to the on-chip processor, as well as rapid debug and prototyping.
This article explores a video design framework that can alleviate some of these challenges and allow for a faster design cycle. The components of the video design framework described can be used collectively or designers can pick and choose to suit an in-house design flow and methodology.
To read the full article, click here
Related Semiconductor IP
- Root of Trust (RoT)
- Fixed Point Doppler Channel IP core
- Multi-protocol wireless plaform integrating Bluetooth Dual Mode, IEEE 802.15.4 (for Thread, Zigbee and Matter)
- Polyphase Video Scaler
- Compact, low-power, 8bit ADC on GF 22nm FDX
Related White Papers
- Using Video-0ver-USB for High Definition recording on mobile handsets
- Processor Architecture for High Performance Video Decode
- High Definition, Low Bandwidth -- Implementing a high-definition H.264 codec solution with a single Xilinx FPGA
- Polyphase Video Scaling in FPGAs
Latest White Papers
- Reimagining AI Infrastructure: The Power of Converged Back-end Networks
- 40G UCIe IP Advantages for AI Applications
- Recent progress in spin-orbit torque magnetic random-access memory
- What is JESD204C? A quick glance at the standard
- Open-Source Design of Heterogeneous SoCs for AI Acceleration: the PULP Platform Experience