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This paper presents a two-layer lossless bit depth scalable coding for RGB color components of high dynamic range (HDR) images. We introduce a two-stage tone mapping, which is composed of a reversible logarithmic mapping and its compensation, to produce a tone mapped LDR image in the base layer. Adding bit stream in the enhancement layer, which car...
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In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-map...
Citations
... Although the baseline method is straightforward and easy to implement, the coding efficiency in the enhancement layer is not satisfactory. To cope with this problem, we introduced a reversible logarithmic mapping and reduced the dynamic range of the HDR images [19,21]. This approach was shown to be effective for compressing data in the enhancement layer. ...
... In this paper, we improve on our previous conference papers [19,21] and add some theoretical analysis. First, we show that a simple extension of the reversible logarithmic mapping (Rev) to the RGBE format degrades the visual quality of the decoded LDR images. ...
... This property also reduces the dynamic range of the mapped integer values. We have experimentally confirmed [21] that the residual in the enhancement layer e R has a lower bit depth than that of e I in the baseline method. We provide the theoretical basis for this observation in Sec. ...
In this paper, we propose a bit-depth scalable lossless coding method for high dynamic range (HDR) images based on a reversible logarithmic mapping. HDR images are generally expressed as floating-point data, such as in the OpenEXR or RGBE formats. Our bit-depth scalable coding approach outputs base layer data and enhancement layer data. It can reconstruct the low dynamic range (LDR) image from the base layer data and reconstructs the HDR image by adding the enhancement layer data. Most previous two-layer methods have focused on the lossy coding of HDR images. Unfortunately, the extension of previous lossy methods to lossless coding does not significantly compress the enhancement layer data. This is because the bit depth becomes very large, especially for HDR images in floating-point data format. To tackle this problem, we apply a reversible logarithmic mapping to the input HDR data. Moreover, we introduce a format conversion to avoid any degradation in the quality of the reconstructed LDR image. The proposed method is effective for both OpenEXR and RGBE formats. Through a series of experiments, we confirm that the proposed method decreases the volume of compressed data while maintaining the visual quality of the reconstructed LDR images.
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... It is well known that extremely long bit depth makes the coding problem difficult [13]. Introducing a lossless version of the lossy logarithmic range reduction [5], lossless two layer systems were proposed [14]- [16]. Even though a user can use arbitrary tone mapping function to produce an LDR from the HDR image, there is no room to optimize it according to the input image. ...
... Since the mapping f in (6) is reversible between x H and x N , both of range reduction and lossless coding of HDR images become possible [13]- [16]. Note that f approximates a logarithmic function as ...
This paper optimizes a tone mapping function in a backward compatible lossless coding of high dynamic range (HDR) images. The mapping function is designed so that it maximizes quality of decoded low dynamic range (LDR) images. An optimization procedure for a lossy coding system is extended to a lossless system under the condition that a user can specify arbitrary tone mapping function to produce an LDR image from the original HDR image. Superiority of the proposed method on the rate distortion curves of the LDR image was confirmed to be up to 7.5 dB in PSNR improvement for an image sample.
... It means that not all the pixel value bins are used in the image. Table I [17][18][19][20] is applied to the OpenEXR images before evaluating the sparseness. ...
This paper proposes a new range reduction method
with the minimum amount of quantization error in the L2 norm
under the L infinity norm constraint. It is necessary to reduce
dynamic range of pixel values of high dynamic range (HDR)
images to have backward compatibility with low dynamic range
image processing systems. The simplest approach is to truncate
lower bit planes in binary representation of pixel values.
However it does not have fine granularity of the reduced range,
and also it does not utilize the histogram sparseness.
Furthermore, it generates significant amount of quantization
errors. In this paper, we propose a new range reduction method
which can 1) utilize the histogram sparseness, and also 2)
minimize variance of the error 3) under a specified maximum
absolute value of the error.
... It means that not all the pixel value bins are used in the image. Table I [17][18][19][20] is applied to the OpenEXR images before evaluating the sparseness. ...
This letter proposes an efficient lossless compression method for high dynamic range (HDR) images in OpenEXR format. The proposed method transforms an HDR image to an indexed image and packs the histogram of the indexed image. Finally the packed image is losslessly compressed by using any existing lossless compression algorithm such as JPEG 2000. Experimental results show that the proposed method reduces the bit rate of compressed OpenEXR images compared with equipped lossless compression methods of OpenEXR format.
