A fixed-point implementation of tone mapping operation for HDR images expressed in floating-point format

Atsushi Tashiro,Masahiro Iwahashi,Hitoshi Kiya,Toshiyuki Dobashi

doi:10.1017/atsip.2014.9

Abstract

A tone mapping operation (TMO) for HDR images with fixed-point arithmetic is proposed. A TMO generates a low dynamic range (LDR) image from a high dynamic range (HDR) image by compressing its dynamic range. Since HDR images are generally expressed in a floating-point data format, a TMO also deals with floating-point data even though resulting LDR images have integer data. As a result, conventional TMOs require many resources such as computational and memory cost. To reduce the resources, an integer TMO which treats a floating-point number as two 8-bit integer numbers was proposed. However, this method has the limitation of available input HDR image formats. The proposed method introduces an intermediate format to relieve the limitation of input formats, and expands the integer TMO for the intermediate format. The proposed integer TMO can be applied for multiple formats such as the RGBE and the OpenEXR. Moreover, the method can conduct all calculations in the TMO with fixed-point arithmetic. Using both integer data and fixed-point arithmetic, the method reduces not only the memory cost, but also the computational cost. The experimental and evaluation results show that the proposed method reduces the computational and memory cost, and gives almost same quality of LDR images, compared with the conventional method with floating-point arithmetic.

Highlights

High dynamic range (HDR) images are diffusing in many fields: photography, computer graphics, on-vehicle cameras, medical imaging, and more
Our method proposes to reduce resources such as computational and memory cost during a tone mapping operation (TMO)
The proposed integer TMO can be applied for multiple HDR image formats by converting the input image to the intermediate format

Summary

INTRODUCTION

High dynamic range (HDR) images are diffusing in many fields: photography, computer graphics, on-vehicle cameras, medical imaging, and more. Our method proposes to reduce resources such as computational and memory cost during a TMO. Real-time processing, such as an HDR video, requires speeding-up or parallelization of computing For these reasons, reducing computational and memory cost regarding a TMO is an important issue. The method in [22] executes the integer TMO with fixed-point arithmetic, and it reduces the computational cost as well. The method can conduct all the calculations of the TMO with only fixed-point arithmetic By these features, the method can be executed under limited resources, such as processors without a FPU or low-memory. The experiments and evaluation confirmed that the proposed method reduces the computational cost and the memory cost, and keeps the quality of tone mapped images, compared to the conventional method with floating-point arithmetic [1]

PRELIMINARIES

PROPOSED METHOD

EXPERIMENTAL AND EVALUATION RESULTS

CONCLUSION