# Video super-resolution
Source: [https://docs.qualcomm.com/doc/80-70022-50/topic/video-super-resolution.html](https://docs.qualcomm.com/doc/80-70022-50/topic/video-super-resolution.html)
The **gst-ai-superresolution** application allows you to generate high resolution
video frames from low-resolution input.
The following figures shows the pipeline, which receives a video stream from a file
source as input, processes it through the super resolution module using LiteRT, and
displays the output.
For information about the plugins used in the pipeline, see Pipeline flow.
Figure : gst-ai-superresolution pipeline (Wayland display)
Figure : gst-ai-superresolution pipeline (file sink)
## Sample model files
Table : Sample model for gst-ai-superresolution
| Runtime | Model files |
| :--- | :--- |
| LiteRT | quicksrnetsmall_quantized.tflite |
## Run the application on the target device
Note: The commands in this section are targeted for the sample
applications based on QLI GA 1.5 (PPA version 05900 in Ubuntu) or later releases.
Run the `apt-cache policy gstreamer1.0-qcom-sample-apps` command to
check your QIM version. If you are using sample applications from older versions,
run the application with the `--help` option for more
instructions.
The sample application uses the
/etc/configs/config-superresolution.json file to read the
input parameters.
To create your own config JSON file, use [config-superresolution.json](https://git.codelinaro.org/clo/le/platform/vendor/qcom-opensource/gst-plugins-qti-oss/-/blob/imsdk.lnx.2.0.0.r2-rel/gst-sample-apps/gst-ai-superresolution/config-superresolution.json) as a
reference.
1. Ensure that you complete the Prerequisites.
2. Update the config JSON file based on the model, input stream, and other
properties. For more information, see Config JSON field description.
3. Use the following format of the config-superresolution.json
file:
{
"input-file-path": "",
"model": "",
"output-file-path": ""
}Copy to clipboard
For example, run the application using the custom video input file and model
paths:
{
"input-file-path": "/etc/media/video.mp4",
"model": "/etc/models/quicksrnetsmall_quantized.tflite"
}Copy to clipboard
4. Run the gst-ai-superresolution
application:
gst-ai-superresolution --config-file=/etc/configs/config-superresolution.jsonCopy to clipboard
5. To display the available help options, run the following command in the SSH
shell:
gst-ai-superresolution -hCopy to clipboard
6. To stop the use case, use CTRL +
C.
## Expected output
The output is displayed on an HDMI monitor.
Figure : Expected output for VSR
## Pipeline flow
The following table lists the plugins used in the video super resolution
pipeline:| Plugin | Description |
| --- | --- |
| filesrc | Captures the video stream and uses tee to split the stream for inferencing. |
| qtimlvconverter | Used by AI processing stream for preprocessing:
Receives the video stream on its sink pad.
Performs the following preprocessing on the stream data. This preprocessing is done when the model expects floating-point values as input.
Color conversion
Scaling (up or down)
Normalization
Converts the preprocessed video stream to a tensor stream on its source pad.
The tensor stream is used for inferencing in the later stages of the pipeline. |
| qtimltflite | Runs on LiteRT and uses the quicksrnetsmall\_quantizedmodel.
The inference runtime receives the tensor stream on its sink pad.
The runtime runs the inference.
Produces a tensor stream with the inference results on its source pad.
|
| qtimlpostprocess | Handles inference results from any super resolution mode.
Loads SRNet module.
Produces results as video frames.
Sends them to the sink pad of qtivcomposer.
|
| qtivcomposer |
Composes frames with contents from its sink pads.
Pushes the GStreamer buffers containing these composed frames to its source pad.
|
| Waylandsink |
Waylandsink submits the video stream received on its sink pad to Weston.
Weston renders the video stream on a local display.
|
## Config JSON field description
The different parameters available to configure the JSON file and run the use case
are as follows:
Table : Field description–config-superresolution.json file
| Field | Values/description |
| :--- | :--- |
| **Input source** | `input-file-path`: The directory path of the input video. |
| **Models** | `model`: The path to the super resolution model. |
| **Output source** | `output-file-path`: The directory path of the output video. If the output-file-path isn't provided, the display output is enabled. |
## Related information
Video super resolution and display with LiteRT
**Parent Topic:** Run AI/ML sample applications
Last Published: Feb 20, 2026
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