HDR Image Compression with Optimized JPEG Coding Azza OULED ZAID SysCom Laboratory, National Engineering School of Tunis University of Tunis El Manar Le belvedere, 1002 Tunisia Email: azza.ouledzaid@ieee.org Amira Houimli Higher Institute of Computer Science University of Tunis El Manar 2080, Ariana, Tunisia Abstract—This paper presents an efficient compression system adapted to High Dynamic Range (HDR) images. First a Tone Mapping Operator (TMO) generates the Low Dynamic Range (LDR) version of the HDR content together with its extra information. The obtained LDR image is encoded using an optimized JPEG coding scheme, whereas the extra information is encoded as side data. Specifically, the optimized JPEG based algo- rithm constructs near-optimal rate-distortion quantization tables using DCT coefficient distribution statistics and Lagrangian optimization approach. To ensure accurate HDR reconstruction, the extra information is compressed with conventional JPEG encoder using the highest quality level. The aim of the proposed HDR coding system is twofold. First, it performs a bit allocation mechanism, to achieve near-optimal rate control. Second, it maintains the backward compatibility with the conventional JPEG. Experiments show that the compression performance of the proposed HDR coder outperforms that of the reference method. I. I NTRODUCTION High dynamic range (HDR) imaging is an attractive way of capturing real world appearance. It allows to preserve the accurate luminance values that can be found in real scenes. Due to the higher luminance range, each pixel is coded as a triple of floating point values, which can range from 10 5 to 10 10 . Such floating point representation induces huge memory and storage requirements. In particular, the size of HDR content is one of the major barrier to be overcome for low space storage and high speed transmission. This critical problem can be resolved by designing efficient compression solutions for HDR images. To promote the wide spread use of HDR images, the backward compatibility with commonly used compression and display technologies is necessary. Since JPEG is currently the most commonly used imaging format, it is obvious that HDR image coding format should be backward compatible with JPEG format to facilitate its inclusion in current imaging ecosystems [1]. Several algorithms have been developed in the literature for the compression of HDR images. Among them, JPEG HDR [2] and HDR JPEG 2000 [3], that are extensions to JPEG and JPEG 2000 compression schemes, respectively. The JPEG HDR algorithm starts with the TM of the HDR content to obtain its 8-bits LDR version. Thereafter, the original HDR image is divided by the LDR one obtaining the ratio image (RI) which is stored as a sub-band. The RI is generally down- sampled to reduce the its size. During the HDR reconstruction, this down-sampling causes halos and/or glare around edges. To alleviate this problem, the authors proposed to introduce cor- rections in the tone mapped image. Despite their effectiveness, theses corrections produce artifacts in the LDR image for the backward compatibility and this cannot be tolerable in many applications. In HDR JPEG 2000 coder [3] the dynamic range of HDR images is reduced using a logarithm with base 2. The obtained values are then remapped in unsigned (16-bit) integers, that are supported by JPEG 2000 standard. JPEG 2000 and JPEG XR also support HDR representations. However their adoption requires a certain investment not always affordable in existing imaging ecosystems as they are not backward compatible with the widely popular JPEG image format [1]. More recently, JPEG working group proposed the JPEG XT standard [4] which extends the JPEG functionalities by enabling support for high dynamic range imaging. Particularly, JPEG XT part 7 divides the image data in LDR JPEG legacy compliant codestream and an extra information. The latter is smartly inserted in the JPEG legacy codestream such that it does not jeopardize backward and/or forward compatibility. Despite the recent developments in the field of HDR image coding and representation, there is a lack of rate-distortion optimization for HDR image compression. To date, no work has been made to develop an HDR compression method that tackles the problem of rate adaptation under communication constraints. JPEG coding scheme is difficult to optimize because of its use of zero runlength coding, which combines zero coefficients from different frequency bands into one symbol. The resulting coupling of different frequency bands prevents the use of classical bit allocation methods, which assume independently coded frequency bands. The key to good compression (in the rate-distortion sense) when using Discrete Cosine Transform (DCT) lies in the quantizer step size selection. The simplest and most commonly used approach is to use a default table and scale it up or down until the desired target rate (or distortion) is reached. Other methods include psycho-visual model based quantization [8], and stochastic optimization techniques [9]. In both approaches, a particular quantization table Q is evalu- 2017 25th European Signal Processing Conference (EUSIPCO) ISBN 978-0-9928626-7-1 © EURASIP 2017 1584