High Dynamic Range Imagery (HDRI) refers to processes, techniques, and digital image systems capable of reproducing real-world lighting and color data with high accuracy. HDRI initially was introduced to overcome dynamic range limitations in digital images, such as described in the scientific publication “Overcoming Gamut and Dynamic Range Limitations in Digital Images” (hereinafter “Ward98—2”), Proceedings of the Sixth Color Imaging Conference, November 1998, as well as in the scientific publication “High Dynamic Range Imagery” (hereinafter “Ward01”), Proceedings of the Ninth Color Imaging Conference, November 2001
HDR pixel data commonly is represented using 96 bit data; 32 bits single precision IEEE floating point data for each RGB component. Standard digital images usually employ 24 bits per pixel; 8 bits for each RGB component. Thus, an HDR image has four times as much data as an LDR image. Therefore, due to relatively large amount of data for an HDR image, there is a need to substantially compress HDR image data.
The publication G. Ward, “Real Pixels,” Graphics Gems II, Ed. by J. Arvo, Academic Press, 1992 (hereinafter “Ward92”), describes the RGBE image format as representing the required dynamic range using 32 bits/pixel instead of 96 bits/pixel, providing a compression ratio of 3:1. Typical image and video compression algorithms, such as JPEG and MPEG, achieve much higher compression ratios, thus producing files hundreds of times smaller than the original source data. The RGBE format explicates a relevant compression problem by introducing the Exponent on a per-pixel basis, since even small errors that may be introduced by common compression methods, such as but not limited to JPEG and MPEG, generate exponentially higher levels of artifacts in the recovered image.
European Patent EP 0991020, owned by the Eastman Kodak Company, (hereinafter, “Kodak Patent”) describes a method to encode the extended color gamut of sRGB images by using residual images obtained by computing
The scientific publication “Perception-motivated High Dynamic Range Video Encoding”, Proc. of SIGGRAPH '04 (Special issue of ACM Transactions on Graphics), 2004 (hereinafter “Mantiuk04”) describes a method to encode HDR video into the MPEG4 standard. As described, the chrominance part of the image is expressed using a u′v′ color space similar to the color space used in the LogLuv image format discussed in Larson, G. W., “LogLuv encoding for full-gamut, high-dynamic range images,” Journal of Graphics Tools, 3(1):15-31 1998 (hereinafter “Ward98”), and also describes using 11 bits to store luminance data directly into the DCT coefficients. To overcome visible artifacts due to the augmented DCT range, a hybrid block coding system using additional data also is introduced
The scientific publication “High Dynamic Range Image and Video Data Compression”, IEEE CG&A, 2005 (hereinafter “Xu05”) describes a method to encode HDR image/video data, where, considering the RGBE image format, the RGB part of the image and the “E” (Exponent) part are separated and sent to different compression schemes. As described, the compression scheme for RGB is lossy while the scheme for “E” is lossless.
The publications “Subband Encoding of High Dynamic Range Imagery”, First Symposium on Applied Perception in Graphics and Visualization (APGV), August 2004, (hereinafter “Ward04”) and “JPEG-HDR: A Backwards-Compatible, High Dynamic Range Extension to JPEG”, Proceedings of the Thirteenth Color Imaging Conference, November 2005, (hereinafter “Ward05”) describe methods/systems for encoding HDR images and video in which a tone mapping operator is applied to the HDR source so that the HDR input is mapped smoothly into a 24 bit output domain with no components being clamped at 0 or 255. The original luminance of the HDR image is divided by the luminance of the tone-mapped LDR version, generating an HDR grayscale residual image which is log-encoded and used to restore the original frame.
The scientific publication “Backward Compatible High Dynamic Range MPEG Video Compression”, Proc. of SIGGRAPH '06 (Special issue of ACM Transactions on Graphics), 25 (3), pp. 713-723, 2006, (hereinafter “Mantiuk06”) discloses a method for compressing HDR video similar to that disclosed in Ward04 and Ward05.
The processes and techniques disclosed in each of the publications identified above disadvantageously call for additional coding and additional per-pixel operations, as well as additional pre- and/or post-correction processing, to achieve satisfactory results. Moreover, currently available MPEG and/or MPEG4 features and filters cannot be fully and/or easily exploited or employed by, or are incompatible with, many of the processes/systems mentioned above.
Consumer digital displays today are only capable of displaying 8 bits for each RGB color component (i.e. 24-bit image data) and, considering luma/chroma separation, are only capable of displaying 8 bits for luma data that contain the high-frequency details of an image. With the introduction of high definition (HD) resolutions and bigger display screens—up to 100 inches—the limited precision of standard 24-bit digital images reveals several artifacts on such displays, even when uncompressed. By employing common compression solutions, like the JPEG or MPEG plethora of algorithms, these artifacts become even more visible to the human eye, due to the reduced precision, in number of bits, of the resulting decompressed output image.
In order to fully exploit the potential of HD resolutions, a display should be capable of showing 1920 shades, i.e. the maximum number of pixels in the longest pixel row at the highest HD resolution (1080p). With 8-bit data it is only possible to display 256 shades and moreover with MPEG compression this figure is usually greatly reduced, producing visible banding artifacts when using HD resolutions even on a 26 inches display. MPEG—and the latest H.264 standard—also introduces block-artifacts due to the nature of the algorithms which encode and store image data in blocks of 4×4 pixels up to 16×16 pixels, which are independent from each other, i.e. the output pixel luma value of a block has no correlation with an adjacent pixel of a different block. This usually leads to a high luminance contrast between edges of the surrounding blocks.
In order to avoid block-artifacts a number of algorithms commonly known as “deblocking filters” have been introduced. Such algorithms though are by nature very expensive in terms of the computing power required, which is linearly related to the dimensions of the image to be filtered. With HD resolutions, the number of pixels to be processed with a deblocking filter is up to 4.6 times the number of pixels being processed in a standard PAL or NTSC frame, thus a deblocking algorithm requires much more computing power in order to be applied to HD resolutions.
Hence, there is also a need to provide an effective method that enables encoding and decoding of higher precision image data (>24 bits/pixel) that is compatible with standard compression methods, such as JPEG and MPEG, and for a method that is able to remove typical image compression artifacts using the lowest possible computing power.