Low-Cost Super-Resolution Algorithms Implementation Over a HW/SW Video Compression Platform

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Low-Cost Super-Resolution Algorithms Implementation over a HW/SW Video Compression Platform 1 Jose 1 Antonio Nu ´ Fco. Lopez, ´ 1 Rafael Peset Llopis,2 Sebastian Lopez, ´ ´ ´ nez, ˜ 1 Gustavo M. Callico, 3 1 Ramanathan Sethuraman, and Roberto Sarmiento 1 The

University of Las Palmas de Gran Canaria, Institute for Applied Microelectronics (IUMA), Tafira Baja, 35017, Spain Consumer Electronics, SFJ-6, P.O. Box 80002, 5600 JB, The Netherlands 3 Philips Research Laboratories, WDC 3.33, Professor Holstlaan 4, 5656 AA Eindhoven, The Netherlands 2 Philips

Received 1 December 2004; Revised 5 July 2005; Accepted 8 July 2005 Two approaches are presented in this paper to improve the quality of digital images over the sensor resolution using superresolution techniques: iterative super-resolution (ISR) and noniterative super-resolution (NISR) algorithms. The results show important improvements in the image quality, assuming that sufficient sample data and a reasonable amount of aliasing are available at the input images. These super-resolution algorithms have been implemented over a codesign video compression platform developed by Philips Research, performing minimal changes on the overall hardware architecture. In this way, a novel and feasible low-cost implementation has been obtained by using the resources encountered in a generic hybrid video encoder. Although a specific video codec platform has been used, the methodology presented in this paper is easily extendable to any other video encoder architectures. Finally a comparison in terms of memory, computational load, and image quality for both algorithms, as well as some general statements about the final impact of the sampling process on the quality of the super-resolved (SR) image, are also presented. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.

1.

INTRODUCTION

Here are two straightforward ways to increase sensor resolution. The first one is based on increasing the number of light sensors and therefore the area of the overall sensor, resulting in an important cost increase. The second one is focused on preserving the overall sensor area by decreasing the size of the light sensors. Although this size reduction increases the number of light sensors, the size of the active pixel area where the light integration is performed decreases. As fewer amounts of light reach the sensor it will be more sensitive to the shot noise. However, it has been estimated that the minimum photo-sensors size is around 50 μm2 [1], a limit that has already been reached by the CCD technology. A smart solution to this problem is to increase the resolution using algorithms such as the super-resolution (SR) ones, wherein highresolution images are obtained using low-resolution sensors at lower costs. Super-resolution can be defined as a technique that estimates a high-resolution sequence by using multiple observations of the scene using lower-resolution sequences. In order to obtain significant improvements in the resulting SR image, some amount of aliasing in the input lowreso