I looked at https://opencolorio.readthedocs.io/en/latest/guides/developing/usage_example... and well, few calls for basic colorspace transform in c++ is not very bad, but of course even with this easy way I still not quite understand where we (cinelerra's) should put this call? At (ffmpeg) decode/encode step? At display step? ===== Applying a basic ColorSpace transform, using the CPU This describes what code is used to convert from a specified source ColorSpace to a specified destination ColorSpace. If you are using the OCIO Nuke plugins, the OCIOColorSpace node performs these steps internally. Get the Config. This represents the entirety of the current color “universe”. It can either be initialized by your app at startup or created explicitly. In common usage, you can just query GetCurrentConfig(), which will auto initialize on first use using the OCIO environment variable. Get Processor from the Config. A processor corresponds to a ‘baked’ color transformation. You specify two arguments when querying a processor: the colorspace_section you are coming from, and the colorspace_section you are going to. cfgcolorspaces_section ColorSpaces can be either explicitly named strings (defined by the current configuration) or can be cfgroles_section (essentially colorspace_section aliases) which are consistent across configurations. Constructing a Processor object is likely a blocking operation (thread-wise) so care should be taken to do this as infrequently as is sensible. Once per render ‘setup’ would be appropriate, once per scanline would be inappropriate. Get a CPU or GPU Processor. Building the processor assembles the operators needed to perform the requested transformation, however it is not ready to process pixels. The next step is to create a CPU or GPU processor. This optimizes and finalizes the operators to produce something that may be executed efficiently on the target platform. Convert your image, using the Processor. Once you have a CPU or GPU processor, you can apply the color transformation using the “apply” function. In C++, you may apply the processing in-place, by first wrapping your image in an ImageDesc class. This approach is intended to be used in high performance applications, and can be used on multiple threads (per scanline, per tile, etc). In Python you call “applyRGB” / “applyRGBA” on your sequence of pixels. Note that in both languages, it is far more efficient to call “apply” on batches of pixels at a time. =====