CUDA Tone Mapping
CUDA tone mapping executes a tone-mapping implementation on an NVIDIA GPU using CUDA parallel computation.
NVIDIA video processing can involve three separate engines or execution roles. Follow a possible decoded-picture route:
| Stage | Work |
|---|---|
| NVDEC | Reconstruct compressed video into decoded surfaces |
| CUDA program | Perform programmable picture calculations, such as tone mapping |
| NVENC | Encode the prepared pictures into another video stream |
The decoder and encoder are hardware video engines. CUDA supplies programmable parallel computation. A tone mapper's kernel defines the brightness and color calculations; those decisions do not come from choosing the NVIDIA brand for decoding or encoding.
Keep a decoded surface accessible to the next stage
NVIDIA's NVDEC API supports decoding to video memory and making the resulting surfaces available for CUDA processing. The application can use those decoded samples as the input to its kernel, then hand the processed result onward through a supported route.
This is a memory and synchronization relationship. The filter must understand the surface's format and layout, wait until decoding has produced it, and ensure its output is ready for the next consumer. Shared access avoids a needless trip through system memory when the complete path supports it.
Now insert a software-only operation between the stages. The application may need to download a representation to system memory, process it there, then upload the result again. The tone kernel can still be fast while those transfers add cost. CPU mapping gives raw-frame storage arithmetic that explains the scale of such traffic.
What video-engine and compute activity indicate
An Emby session can use an accelerated encoder while another stage handles tone mapping. Video-encode activity then describes NVENC's job, not the location of every preceding calculation. Different monitoring views can aggregate or separate video and compute activity.
To understand the session, identify its decoder, filter, frame transfers, and encoder. The hardware overview supplies the product's platform context; the actual application implementation chooses which supported operations use CUDA.
The kernel's chosen curve, input peak, color interpretation, and output target determine the prepared picture. Comparing these inputs alongside the memory route explains both the image result and the execution cost without treating one activity percentage as a description of the entire conversion.