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Named as Edge AI and Vision
2026 Product of the Year
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For decades, the image signal processor was one of the least visible, most important pieces of a camera system.
The sensor captured raw Bayer data. The ISP turned it into an image. Engineers spent months tuning it. Computer vision algorithms consumed the output.
That model made sense when the goal was simple: make the image look good.
Physical AI changes the question.
Robots, autonomous machines and intelligent edge devices don't need images that simply look good to humans. They need the right information to perceive the physical world reliably.
And that makes the ISP a very different kind of technology.
NVIDIA’s Jetson Thor uses SIPL, a modular software framework for camera integration, streaming and control, alongside hardware ISP processing.
At Visionary.ai, we see an opportunity to make image processing itself more programmable: giving developers control over how raw sensor data is transformed for their applications, with pipelines that can improve through software updates.
A conventional ISP is essentially a sophisticated collection of algorithms and parameters.
Demosaicing. Noise reduction. HDR. Sharpening. Color correction. Tone mapping.
Each individual function is manageable. The complexity comes from making all of them work together across thousands of combinations of sensor characteristics, lenses, lighting conditions and motion.
And someone has to tune it.
Change the sensor, and the tuning changes.
Change the lens, and it changes again.
Move from daylight to extreme low light, introduce motion, or add a high-dynamic-range scene, and suddenly the compromises become visible.
The result is an enormous engineering effort devoted to answering a deceptively simple question:
What should the camera output?
Traditionally, the answer has been optimized around human vision.
But Physical AI has a different objective.
The best image for a person is not necessarily the best image for an object detector.
An image that looks clean may have lost texture.
An image that looks sharp may contain artifacts that confuse a neural network.
An image that looks bright may no longer contain the information needed to distinguish an object from its background.
For AI, "good image quality" needs a new definition.
This is part of a much broader technology transition.
Computing has repeatedly moved away from fixed-function hardware toward programmable architectures.
CPUs replaced specialized machines.
GPUs transformed graphics into programmable parallel computing.
Networking became software-defined.
And now imaging is beginning to follow the same path.
The reason is not simply flexibility.
It is the speed of change.
Sensors evolve.
AI models evolve.
Robots evolve.
Applications evolve.
A hardware pipeline designed today cannot anticipate every imaging requirement of the next five years.
Software can be updated.
That doesn't make hardware irrelevant. Far from it. Dedicated hardware remains critical for achieving the power, latency and throughput required at the edge.
But it changes where intelligence belongs.
The hardware should provide the compute. The software should define what the compute does.
This is also where the emergence of software ISPs becomes particularly interesting.
Visionary.ai is taking the idea of a software-defined camera pipeline to its logical conclusion: using neural networks to process raw sensor data into an image that is not only visually better, but more useful for downstream computer vision.
The distinction is important.
This isn't simply about replacing one implementation of an ISP with another.
It is about changing the optimization target.
Instead of building an image pipeline around a fixed set of manually tuned rules, a neural ISP can learn the transformation from raw sensor data to usable visual information.
That opens up a fundamentally different model for camera development.
The ISP can become software.
It can run on the same edge AI infrastructure already being deployed for perception.
It can be adapted to different sensors and environments.
And it can evolve through software updates rather than waiting for the next generation of silicon.
Visionary.ai is one of the companies pushing this model forward, with an AI ISP designed to run in real time on edge AI hardware and transform raw Bayer data into perception-ready imagery.
There is a deeper implication here.
We tend to think of computer vision as beginning with the neural network.
It doesn't.
It begins with the photons that reach the sensor.
Everything that happens between the sensor and the perception model determines what information the AI ultimately gets to work with.
If low-light detail disappears at the camera.
If motion creates artifacts.
If HDR information is lost.
If noise overwhelms small objects.
The neural network downstream is starting with a compromised representation of reality.
Better AI therefore doesn't only mean better models.
It means better input.
This is why the ISP is becoming strategically important again.
Not as a piece of camera plumbing, but as the first layer of the perception stack.
The transition underway in imaging is bigger than the ISP.
The camera is evolving from a passive sensor into an intelligent, programmable system.
NVIDIA's direction with Jetson is an important signal of that transition.
And the emergence of AI ISPs points toward where it can go next.
The question is no longer simply:
"How do we process the image?"
It is:
"What information does the machine need from the image?"
That is a very different question.
And answering it may ultimately determine how well Physical AI can see, understand and interact with the real world.
The future of camera perception won't be defined by better fixed-function pipelines.
It will be defined by software.