A Format for Digital Preservation of Images: A Study on JPEG 2000 File Robustness

Digital preservation requires a strategy for the storage of large quantities of data, which increases dramatically when dealing with high resolution images. Typically, decision-makers must choose whether to keep terabytes of images in their original TIFF format or compress them. This can be a very difficult decision: to lose visual information though compression could be a waste of the money expended in the creation of the digital assets; however, by choosing to compress, the costs of storage will be reduced. Wavelet compression of JPEG 2000 produces a high quality image: it is an acceptable alternative to TIFF and a good strategy for the storage of large image assets. Moreover, JPEG 2000 may be considered a format that can guarantee an efficient robustness to bit errors and offers a valid quality with transmission or physical errors: this point of view is confirmed by the case study results that we report in this article, concerning image quality after occurrence of random errors by a comparison among different file formats. Easy tools and freeware software can be used to improve format robustness by duplicating file headers inside or outside the image file format, enhancing the role of JPEG 2000 as a new archival format for high quality images. Introduction: current trends In recent years the JPEG 2000 format has been widely used in digital libraries, not only as a "better" JPEG to deliver medium-quality images, but also as new "master" file for high quality images, replacing TIFF1 images. One of the arguments used for this policy was the "lossless mode" feature of JPEG 2000; but this type of compression saves only about the half of the storage http://www.dlib.org/dlib/july08/buonora/07buonora.html requirements of TIFF, so it is unlikely that this was the only reason that digital libraries moved in this direction. The only reasonable choice was the standard lossy compression, which offers a 1:20 (color) or 1:10 (grayscale) ratio. This provides a significant savings in terms of storage, considering that the quality of images in digitization projects has increased dramatically in the past few years: the highest standards for image capture are now very common in digital libraries. Thus, the argument turned from the "mathematically lossless" concept to a softer "visually lossless" definition, and the question became: what do we lose in choosing the JPEG 2000 "lossy" mode? Let's focus on the following definitions: "The image file will not retain the actual RGB color data, but it will look the same because screens and our eyes are so forgiving"2 "... many repositories are storing "visually lossless" JPEG 2000 files: the compression is lossy and irreversible but the artifacts are not noticeable"3 As mentioned above, some institutions began to store JPEG 2000 files in their digital repositories as the "archival format"4. This policy was sometimes officially declared, or in some cases was adopted de facto. "The migration process involves creating a derivative master from the original archival master..." or, as shown in the example of the following migration rationale: " Create JPEG 2000 datastream for presentation and standardize on JPEG 2000 as an archival master format. "5 One of the most relevant and specific examples of format migration to JPEG 2000 was made at the Harvard University Library (HUL):6 "HUL chose to perform a migration of various image files to the JPEG 2000 format. There is great local interest at Harvard in the retrospective conversion of substantial numbers of existing TIFF images to enhance their utility by permitting the dynamic image manipulation facilitated by the JPEG 2000 format. The three goals that guided the design of the migration were: To preserve fully the integrity of the GIF, JPEG, and TIFF source data when transformed into the JPEG 2000 (JP2) format To maximize the utility of the new JP2 objects To minimize migration costs" The Xerox Research Center, namely Robert Buckley, was involved in this strategy, producing studies about the integration of JPEG 2000 in the OAIS Reference Model and defining it as a digital preservation standard.7 Although Buckley's Technology Watch Report has been accepted and promoted by the Digital Preservation Coalition in the UK, many relevant experts in this http://www.dlib.org/dlib/july08/buonora/07buonora.html field still seem to show some skepticism and continue to take a "wait and see" position:8 "... some institutions engaged in large-scale efforts are considering a switch to JPEG 2000 ... However, the standard is not yet commonly used and there is not sufficient support for it by Web browsers. The number of tools available for JPEG 2000 is limited but continues to grow".9 Tim Vitale's opinion on JPEG 2000 was very clear in his 2007 report:10 "It is not an archival format ... Existing web browsers (mid-2007) are not yet JPEG 2000 capable. One of the biggest problems with the format is the need for viewing software to be added to existing web browsers ... There are very few implementations of the JPEG 2000 technology, more work needs to be done before general understanding and acceptance will be possible." However, this is no longer the case: most common commercial, digital imaging programs now support JPEG 2000, not to mention JPEG 2000 support by some excellent shareware.11 The real problem is that the JPEG 2000 format allows the storage of very large images, and no current programs can manage the computer memory in an intelligent way: this is the commercial reason for professional image servers and encoders, which are relatively costly,12 or specific viewers for geographic images (generally free13), or browser plug-ins (free as well). 1. Image compression of continuous tone images The primary objection to JPEG 2000 compression remains the possible loss of visual information. Our approach in arguing against this will not focus on how the wavelet approach works,14 but why it works, with some very basic elements of compression theory.15 In other words, preserving visual information deals mainly with how the images are perceived visually, and only secondarily deals with the mathematical aspects of the physical signal (materials, procedures, techniques). Some would argue that images look the same as they did before compression simply because humans don't see very well, and that a deeper examination (or a better monitor) would reveal errors and losses. This is not true: even when JPEG 2000 images are enhanced by magnification, no human could perceive any errors or losses. A digital surrogate is not necessarily a bad copy of the original, and compression does not always mean loss of information. Some people also may think that compression is the equivalent of the "sampling" of a signal; for example, if we choose 300 points per inch to represent an object, sub-sampling might take only 150 or 100 points instead, which creates the risk of losing some information essential for reconstructing that signal. Any sampling below the Nyquist rate produces aliasing effects: if we represent the signal as a wave, the sampling interval should match exactly the shape of the wave. Otherwise, original images are "misunderstood" and appear as artifacts. But compression is not a kind of sub-sampling made after the capture of an image. http://www.dlib.org/dlib/july08/buonora/07buonora.html We can either eliminate redundant information (a sequence of identical values), or we can have some kind of lossless compression, but below the physicalmathematical reality, we can operate on the human perception of it. Since we are dealing with the information that we perceive with our eyes, we can compress irrelevant information, i.e., what is less relevant to our senses. The human eye is less sensitive to colors than to light, so the chrominance signal can be compressed more than the luminance signal can, without any loss of perception. This is very important with digital images of historical documents, as they are usually either color or grayscale images, i.e., "continuous tone" images. As opposed to a "discrete tone" image (as a printed or typed document in black and white), in a continuous tone image any variation of adjacent pixels is relevant: in other words, pixels are "correlated" with each other. We cannot retrieve a sequence of identical values to compress, and we need a more sophisticated strategy. We can select a part of the image, an array of pixels, and calculate the average of the values; then, we can calculate the difference of any single value from the average. This is called "de-correlation" of the image pixels, and at the end of this process we will find that many of the differences from the basic average value are 0, or almost zero, so we can easily compress the image by assigning them the same values (quantization).16 When we separate the three color channels, each of them can be considered as a grayscale image, and we can use the "bit planes" technique.17 For example, let us take three adjacent pixels in a grayscale image, with very different values, in a decimal and in a binary code: Figure 1: An 8-bit grayscale image and its bit planes. 10 = 000001010 3 = 000000011 -7 = 100000111 The image is at 8-bit depth, so we have 1+8 bits (the first represents +/sign). At positions 2,3,4,5 (i.e. at bit-plane 2,3,4,5) we find only "0", and at position 8 find only "1": this is also expressed by saying that the relevance of the information or energy (low frequencies) concentrates at certain levels, and the other levels (high frequencies) can be easily compressed.18 This is very clear in http://www.dlib.org/dlib/july08/buonora/07buonora.html the following representation of an image in 8-bit planes: continuous tone variations between adjacent pixels are now turned in eight separate contexts, where it is now possible to compress adjacent values. Figure 2: A corrupted JPEG file. There are two main methods for de-correlating pixels: orthogonal transform and subband transform. The concept of "transform" is easy to understand from a geometrical point of view: a transform, as a reflection, "is a mathematical concept, but it is not a shape, a number, or a formula"; it is more "a way to move things in space"19 to operate the Hi/Lo frequency separation mentioned above. The DCT (discrete cosine transform) is the typical orthogonal transform that has been used in JPEG compression for many years. In the JPEG compression, color images are decomposed in an YCbCr color space (Y is the luma, or the brightness in an image, Cb and Cr are blue and red chroma components, respectively): the luminance component is the most relevant to human eyesight, so it is less compressed than the other two components. JPEG can use DCT to break up an image to its spatial frequency components, and it compresses the low-frequency component first. This important – but optional – feature is called progressive encoding. Unfortunately, JPEG is generally used in a sequential mode, rather than in a progressive mode, so when data is corrupted, the encoding/decoding process fails and the rest of the image is lost.