MARDEN, Kent (April 4, 2012) – The Pixel Farm Ltd (www.thepixelfarm.co.uk), developer of innovative image-processing systems for VFX, DI and restoration, today announced their new Stream Pre-Processing Module for PFClean.
Building on a tradition of innovation in the field of GPU processing, The Pixel Farm are set to further extend their lead over the competition by leveraging the power of CUDA on the NVidia platform to deliver lightning fast pre-processing of core restoration tasks at speeds from realtime at SD up to 10 FPS at full 2k 10 bit*
"Feedback from our user base has indicated that the need for speed is greater than ever before with today’s industrial scale content conservation workflows making ever greater throughput demands.” Says Dan Harvey, DI and Restoration Product Manager. “Tasks that traditionally required costly signal throughput devices, can now be performed on the GPU, providing dedicated hardware style performance on standard workstation class hardware."
Designed to meet this challenge, PFClean’s eagerly awaited Stream Pre-Processing Module makes short work of repetitive and traditionally time consuming restoration tasks with significant price performance benefits over bespoke competing hardware options.
Initially shipping with Degrain, Deflicker, Dirt / Defect Removal and LUT burn-in, the Stream Pre-Processing Module is available as an add-on option within the the PFClean restoration and remastering application. Delivering results faster than ever for direct export, or allowing results to be cached for further refinement with PFClean’s unrivalled suite of semi-automated and manual tools, the Stream Pre-Processing Module offers a dramatic leap forward for productivity.
At NAB 2012, we will be demonstrating the Stream Pre-Processing Module running on a range of platforms, from standard computers through to our next-generation Turnkey Systems, the most compact and cost effective solutions for 4K realtime uncompressed playback, grading and restoration.
*Hardware dependent
Topics: Press Release City
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