# Video Encoding and Decoding Optimization

For AI purposes, video files are processed as raw video frames but for streaming and storing
video material, it makes more sense to use compressed video formats such as H.264, H.265, and VP9.
This means that converting video - decoding and encoding it - becomes an important part of
every AI system involving either live or pre-recorded material.

Since Intel offers **best-in-class hardware for encode/decode operations**, Open Edge Platform
provides great performance for your video workloads, mostly due to optimizations offered with
[DL Streamer](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dl-streamer/index.html).

It enables you to **maximize concurrent streams**, decoding as many video streams as possible
simultaneously, and **use resources efficiently**, by minimizing CPU/GPU load, memory bandwidth,
and copy operations. What's important, it also helps to find the right balance between
latency and throughput.

|                   | **Low latency**                                                           | **High throughput**                                                                      |
|-------------------|---------------------------------------------------------------------------|------------------------------------------------------------------------------------------|
| Best For          | Real-time analytics or monitoring.                                        | Batch video ingestion and processing pre-recorded material.                              |
| Benefits          | Get immediate results for single frames.                                  | Process very large batches of frames at once, fully utilizing GPU bandwidth and multiple streams. |
| How it's achieved | Decoding frames one-by-one or in minimal batches, with minimal buffering. | Decoding multiple frames or streams in batches, with pipeline parallelism.               |
| Trade-offs        | May underutilize GPU, which works better with larger chunks of data.      | Shows results only once they are all processed.                                          |

In DL Streamer, running these operations is as easy as adding two lines of code, for example:

decoding:

```bash
filesrc location=input.mp4 ! decodebin ! videoconvert ! video/x-raw,format=BGRx ! appsink
```
encoding:

```bash
... ! videoconvert ! vaapih264enc ! mp4mux ! filesink location=output.mp4
```

You will find a detailed description in the
[DL Streamer Performance Guide](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dl-streamer/dev_guide/performance_guide.html).