JPEG
Real-Time FPGA JPEG Compression for High-Performance Camera Streams
View DetailsGidel FPGA-based image compression IPs deliver real-time compression for demanding Imaging & Vision applications. Designed for ultra-high throughput, low latency, and efficient FPGA resource utilization, they reduce bandwidth, storage requirements, and system bottlenecks across industrial, medical, defense, and scientific imaging systems.
| Encoder Name | JPEG | LL | Q+ |
|---|---|---|---|
| Compression Method | Standard JPEG compression | Bit-perfect lossless compression | Proprietary compression that maintains original SNR |
| Main Benefit | Selectable compression quality with broad compatibility |
Fully reconstructs the original image data |
Maintains original SNR while improving compression ratios |
| Supported Image Formats |
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| Compression Ratio | Selectable according to image-quality requirements |
1:2.3 in real-world video tests at 8 bits/pixel |
Up to 10:1+ |
| Throughput | Beyond 1.8 GPixels/s for 4:2:2 sampling |
Beyond 1 Gpixel/s | Beyond 1.2 GPixels/s |
| Latency | As low as 130 μs | Real-time compression | Less than one frame period |
| Compression Modes | Individual JPEG images | Frame and video compression using I and P frames |
Single-frame and continuous video compression |
| Camera Setup |
Optimized for moving cameras | Moving and static cameras | Optimized for static cameras |
| Supported Bit Depth |
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| Supported Platforms | Available on All Gidel FPGA Platforms | ||
Gidel FPGA platforms can combine real-time compression with image enhancement and processing directly within the acquisition flow. Optional FPGA processing includes HDR, detection, and other image enhancements. Customers can also integrate custom algorithms alongside Gidel IPs. This modular approach reduces host CPU and GPU load while maintaining low-latency performance for Imaging & Vision systems.
Need more information?
Block Diagram: Example for Compression in the FPGA data flow
As today’s cameras deliver higher resolutions and frame rates and new systems architectures such as vision on the edge have bandwidth limitations, compression becomes a key challenge for advanced image processing applications. But how can the data throughput be reduced without jeopardizing the image and inspection quality?
Demo at the Embedded World 2024 show
InVision TechTalk webinar held on September 2023
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