JPEG2000 Image Compression Fundamentals, Standards and Practice


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Try again later. Citations per year. Duplicate citations. The following articles are merged in Scholar. Their combined citations are counted only for the first article. Merged citations. This "Cited by" count includes citations to the following articles in Scholar. Add co-authors Co-authors. Upload PDF. Follow this author. Correspondence to BryanE Usevitch. Reprints and Permissions. Search all SpringerOpen articles Search. Abstract Many scientific applications require that image data be stored in floating-point format due to the large dynamic range of the data.

References 1. Google Scholar 2. Google Scholar 3. Each image is identified with pathology or satellite together with an index, e. Recent technological developments in digital pathology allow rapid processing of pathology slides using array microscopes [ 24 ]. The resulting high-resolution images referred to as virtual slides can then be reviewed by a pathologist either locally or remotely over a telecommunications network. Due to the high resolution of the imaging process, these images can easily occupy several GBytes.

Thus, remote examination by the pathologist requires efficient methods for transmission and display of images at different resolutions and spatial extents at the reviewing workstation.

The satellite images employed here show various locations on Earth before and after natural disasters. The images were captured by the GeoEye-1 satellite, at 0. These images are also so large that fast rendering and significant bandwidth savings are essential for efficient remote image browsing. Reference images for intermediate resolutions were obtained by downscaling the next higher native resolution reference image. In what follows, the statement that a decompressed reduced resolution image is visually lossless means that it is visually indistinguishable from its corresponding reference image.

To evaluate the compression performance of the proposed method, each image was encoded using three different methods. The first method is referred to here as the 6-layer method. The second method, referred to as the layer method, uses a total of 24 layers to provide visually lossless quality at each of the six native resolutions, plus three intermediate resolutions below each native resolution. The third method, used as a benchmark, employs the method from [ 22 ] to yield a visually lossless image optimized for display only at full resolution.

The codestream for this method contains a single layer, so this benchmark is referred to as the single-layer method. Figure 5 compares the number of bytes that must be decoded transmitted for each of the three coding methods to have visually lossless quality at various resolutions.

Results are presented for one image of each type. Graphs for other images are similar. The number of bytes for the single-layer, 6-layer, and layer methods at each resolution are denoted by crosses, rectangles, and circles, respectively. As expected, the curves for the single-layer method generally lie above those of the 6-layer method, which in turn generally lie above those of the layer method. It is worth noting that the vertical axis employs a logarithmic scale, and that gains in compression ratio are significant for most resolutions.

Number of bytes required for decompression by the three described coding methods. The single-layer, 6-layer, and layer methods at each resolution are denoted by crosses, rectangles, and circles, respectively. Table 7 lists bitrates obtained in bits-per-pixel with respect to the dimensions of the full-resolution images averaged over all 15 test images. From this table, it can be seen that the 6-layer method results in These significant gains are achieved by discarding unneeded codestream data in the relevant subbands in a precise fashion, while maintaining visually lossless quality in all cases.

Specifically, the 6-layer case can discard data in increments of one layer out of 6, while the layer method can discard data in increments of one layer out of In contrast, the single-layer method must read all data in the relevant subbands. Specifically, for the single-layer method, the image is downscaled from all LL , 3 data which amounts to 5. For the proposed methods, the image is downscaled from 3 of 6 layers and 10 of 24 layers of LL , 3 data which amounts to 2. Although the the proposed methods offer significantly lower bitrates, all three resulting images have the same visual quality.

That is, they are all indistinguishable from the reference image. Satellite image courtesy of GeoEye. Although this multi-layer method provides significant gains for most resolutions, there exist a few negligible losses for some resolutions.

JPEG2000 Compatible Lossless Coding of Floating-Point Data

Specifically, the 6-layer case is slightly worse than the single-layer case for LL , 0 as well as for the three intermediate resolutions immediately below LL , 0. The average penalty in this case is 0. Similarly, the layer case is slightly worse than the 6-layer case at each of the six native resolutions. These minor drops in performance are due to the codestream syntax overhead associated with including more layers. As mentioned previously, the layer functionality of JPEG enables quality scalability.

As detailed in the previous paragraph, for certain isolated resolutions, the single-layer method provides slightly higher compression efficiency as compared to the 6-layer and layer methods. However, it provides no quality scalability and therefore no progressive transmission capability.

To circumvent this limitation, layers could be added to the so called single-layer method. To this end, codestreams from the single-layer method were partitioned into six layers. The first five layers were constructed via the arbitrary selection of five rate-distortion slope thresholds, as normally allowed by the Kakadu implementation. The six layers together yield exactly the same decompressed image as the single-layer method. Table 8 compares the bitrates for decoding LL , 0 under the proposed 6-layer visually lossless coding method vs.

As seen from the table, the results are nearly identical. Results for other images as well as for layers are similar. Bitrates bpp of the proposed 6-layer method vs. To verify that the images encoded with the proposed scheme are visually lossless at each resolution, a three-alternative forced-choice 3AFC method was used, as in [ 23 ]. Two reference images and one compressed image were displayed side by side for an unlimited amount of time, and the subject was asked to choose the image that looked different. The position of the compressed image was chosen randomly.

In the validation experiments, all compressed images were reconstructed from codestreams encoded with the layer method. Validating the layer method suffices since this method uses the same or larger threshold values than those of the 6-layer or single-layer method at each resolution. In other words, the layer method never uses less aggressive quantization than the other two methods.

All 15 images were used in the validation study. These five resolutions form a representative set spread over the range of possible values. The validation experiment consisted of four sessions — one for each resolution tested. In each session, all 15 images were viewed five times at the same resolution 75 trials for each subject in random order. Subjects were allowed to rest between sessions.

Twenty subjects, who are familiar with image compression and have normal or corrected-to-normal vision, participated in the experiment. Each subject gave a total of responses over the four sessions. The validation was performed under the same viewing conditions as in [ 23 ] Dell U LCD monitor, ambient light, a viewing distance of 60 cm.

During the experiment, no feedback was provided on the correctness of choices. Based on these results, it is claimed that the proposed coding method provides visually lossless quality at every tested resolution. It is worth noting that for medical images, such as the pathology images employed as part of the test set herein, visually lossless compression may not be the most relevant goal. Indeed, our ongoing work is focused on the maximization of compression efficiency while maintaining diagnostic accuracy. JPIP is a connection-oriented network communication protocol that facilitates efficient transmission of images using the characteristics of scalable JPEG codestreams.

A user can interactively browse spatial regions of interest, at desired resolutions, by retrieving only the corresponding minimum required portion of the codestream. Figure 7 shows a block diagram of a JPIP remote image browsing system. First, the client requests the spatial region, resolution, number of layers, and image components of interest using a simple descriptive syntax to the server. In response to the client request, the server accesses the relevant packets from the JPEG codestream and sends them to the client.

The client decodes the received packets and renders the image. Through a graphic user interface GUI on the client-side, a user can request different regions, resolutions, components, and number of layers at any time.

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To minimize the number of transmitted packets and maximize the responsiveness of interactive image browsing, the server assumes that the client employs a cache to hold data from previous requests, and maintains a cache model to keep track of the client cache. If a request is found to contain cached data, the server does not re-send that portion of the data. The experimental results described below were obtained using the same codestreams employed in the experiments described in Section 5.

Table 10 reports the number of bytes transmitted via JPIP for 4 different images and 3 different visually lossless coding methods. The dimensions size of each image are included in the table as well. The rows in the table correspond to different images, while the 3 rightmost columns correspond to the different coding methods.

Jpeg image compression fundamentals standards and practice

The same sequence of browsing operations was issued by the JPIP client for each image and each coding method. This resulted in an overview of the entire image being displayed in a window of size 0. This initial window size was maintained throughout the browsing session of the image under test. Three pan operations were included after each new location request. As mentioned above, only the appropriate layers were transmitted for the 6-layer and layer methods. As expected, in each case, the number of bytes required by the 6-layer method was significantly less than that required for the single-layer method.

In turn, the layer method resulted in significant savings over the 6-layer method.

Averaged over the four images, the number of transmitted bytes for the single-layer method was It is clear from these results that the codestreams encoded by the 6-layer and layer methods require considerably less bandwidth than a codestream optimized only for full resolution display. Transmitted bytes for the three visually lossless coding methods while remotely browsing compressed images.

As mentioned in Section III, layers are essential to effective browsing of remote images. Specifically, no quality progressivity is possible for single-layer codestreams.

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Figure 8 demonstrates this via example images obtained via JPIP browsing of a single-layer file vs. While neither image is yet visually lossless 1 , for the number of bytes transmitted up to the moment of rendering in the figure, the advantage of progressive transmission is readily apparent. Using the image from the layer file, the user may be able to make their next request, thus preempting the current request, without waiting for the rest of the data to be transmitted.

This can further reduce bandwidth, but was not used to obtain the values in Table Images rendered while the image data are being retrieved via a JPIP client. This paper presents a multi-resolution visually lossless image coding method using JPEG, which uses visibility threshold values measured for downsampled JPEG quantization distortion. This method is implemented via JPEG layers. Each layer is constructed such that the maximum quantization error in each codeblock is less than the appropriate visibility threshold at the relevant display resolution.

The resulting JPEG Part 1 compliant coding method allows for visually lossless decoding at resolutions natively supported by the wavelet transform as well as arbitrary intermediate resolutions, using only a fraction of the full-resolution codestream. In particular, when decoding the full field of view of a very large image at various reduced resolutions, the proposed 6-layer method reduces the amount of data that must be accessed and decompressed by 30 to 50 percent compared to that required by single-layer visually lossless compression.

Jpeg2000 image compression fundamentals standards and practice

This is true despite the fact that the single-layer method does not access nor decode data from unnecessary high frequency subbands. The layer method provides additional improvements over the 6-layer method ranging from 25 to 40 percent. This in turn, brings the gains of the layer method over the single-layer method into the range of 55 to 65 percent. Stated another way, the gain in the amount of data access and decompressed effective compression ratio is improved by a factor of more than 2. These gains are born out in a remote image browsing experiment using digital pathology images and satellite images.

JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice
JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice
JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice
JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice
JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice
JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice
JPEG2000 Image Compression Fundamentals, Standards and Practice JPEG2000 Image Compression Fundamentals, Standards and Practice

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