Video Super Resolution
gst-ai-superresolution uses an AI model to perform super-resolution reconstruction on low-resolution video, generating high-resolution output frames.
Uses the QuickSRNet Small model (~42 KB), suitable for real-time inference.
Prerequisites
- Completed QIM SDK Installation and Model Download
Steps
1. Verify Model
ls -l /etc/models/quicksrnetsmall_quantized.tflite
Super resolution does not require a label file.
2. View Configuration
cat /etc/configs/config-superresolution.json
Key fields:
| Field | Default | Description |
|---|---|---|
input-file-path | /etc/media/video.mp4 | Input video path |
Note: The super resolution config uses
input-file-path(notfile-path).
3. Run
gst-ai-superresolution --config-file=/etc/configs/config-superresolution.json
Press Ctrl + C to stop.
Expected Output
Terminal output:
Running app with model: /etc/models/quicksrnetsmall_quantized.tflite
VERBOSE: Replacing 10 out of 13 node(s) with delegate (TfLiteQnnDelegate) node, yielding 3 partitions for the whole graph.
Pipeline state changed from PAUSED to PLAYING
The display shows the super-resolved video output with clearer details compared to the original input.
Validation
Replacing 10 out of 13 node(s): 10 operators delegated to DSP, 3 fall back to CPU- Pipeline reaches
PLAYINGstate - Display shows super-resolved video
Some operators (Reshape, Transpose) are not supported on DSP and automatically fall back to CPU. This does not affect overall functionality.
How It Works
QuickSRNet is an ultra-lightweight super-resolution network designed for mobile and embedded devices. Pipeline flow:
filesrc → qtdemux → h264parse → v4l2h264dec
↓
qtimlvconverter
↓
qtimltflite (DSP + CPU)
↓
qtivcomposer
↓
waylandsink