An Experimental Evaluation of Debayering Algorithms on GPUs for Recording Panoramic Video in Real-Time

An Experimental Evaluation of Debayering Algorithms on GPUs for Recording Panoramic Video in Real-Time

Ragnar Langseth (ForzaSys AS, Oslo, Norway), Vamsidhar Reddy Gaddam (University of Oslo and Simula Research Laboratory, Oslo, Norway), Håkon Kvale Stensland (University of Oslo and Simula Research Laboratory, Oslo, Norway), Carsten Griwodz (Simula Research Laboratory, Oslo, Norway), Pål Halvorsen (University of Oslo and Simula Research Laboratory, Oslo, Norway) and Dag Johansen (Univeristy of Tromsø - The Arctic University of Norway, Tromsø, Norway)
DOI: 10.4018/ijmdem.2015070101
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Abstract

Modern video cameras often only capture a single color per pixel in a single pass operation. This process is called ltering, where pixels are ltered through a color lter array, and the Bayer lter is perhaps the most common lter used today. This means that the missing color channels must be restored in the image or the video frame in a post-processing step, i.e., a process referred to as debayering. In a live video scenario, this operation must be performed eciently in order to output each video frame in real-time, while also yielding acceptable visual quality. Here, the authors evaluate debayering algorithms implemented on a GPU for real-time panoramic video recordings using multiple 2K-resolution cameras.
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2. System Overview

We have earlier described our panorama pipeline used in the Bagadus system [Halvorsen et al., 2013, Stensland et al., 2014]. In this system, we record raw 2040 × 1080 video frames in Bayer format from five cameras at 50 fps, and each of these camera streams must be debayered in real-time.

Modern GPUs can provide significantly better performance than a CPU for certain tasks. They are optimized for applying small transformations to every single pixel or texture element, with hundreds or thousands of threads performing the same task in parallel, with minimal inter-thread communication. Debayering is an inherently parallel operation, as each pixel, or block of pixels, can typically be calculated locally. Hence, with our high data rate and real-time requirements, we found the GPU to be better suited to perform this task. However, more complex algorithms that require a greater level of contextual information about the entire image will not achieve the same performance increase.

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