JPEG 2000 performance evaluation and assessment
Introduction
The standardization effort of the next ISO/ITU-T standard for compression of still images, JPEG 2000 [9], has recently reached the International Standard (IS) status, 4 years after the call for proposals [10]. Great efforts have been made by all the participants to deliver a new standard for today's and tomorrow's applications, by providing features inexistent in previous standards, but also by providing higher efficiency for features that exist in others. Now that the standard has been finalized and accepted some trivial questions would be: how well does it perform, what are the features offered by JPEG 2000 and how well are they fulfilled, when compared to other standards offering the same or similar features. This paper aims at providing an answer to this simple but somewhat complex question. Section 2 provides a brief overview of the techniques compared, Section 3 evaluates different aspects of compression efficiency, while Section 4 looks at the algorithms’ complexities. Error resilience performance is studied in Section 5 and functionality in Section 6. Finally conclusions are drawn in Section 7.
Section snippets
Overview of evaluated algorithms
For the purpose of this study we compare the coding algorithm in the JPEG 2000 standard to the following three standards: JPEG [17], MPEG-4 Visual Texture Coding (VTC) [7] and JPEG-LS [6]. In addition, we also include SPIHT [20] and PNG [25]. The reasons behind this choice are as follows. JPEG is one of the most popular coding techniques in imaging applications ranging from Internet to digital photography. Both MPEG-4 VTC and JPEG-LS are very recent standards that start appearing in various
Compression performance
When evaluating image-coding algorithms, there are several factors that determine the choice of a particular algorithm for an application. An important one in most cases is compression efficiency, which we evaluate in this section. However, there are other factors, such as the set of provided functionality and the complexity, that can be even more determining than pure compression efficiency. Those will be evaluated in subsequent sections.
Compression efficiency is evaluated for lossless and
Complexity
Besides compression efficiency, another important aspect of an image compression system is the complexity of the algorithm execution. However, depending on the application and the working environment, the complexity depends on different factors. They can be memory bandwidth, total working memory, number of arithmetic operations, number of hardware gates, etc. Furthermore, these numbers are very dependent on the optimization, targeted applications and other factors of the different
Error resilience
In the recent years there has been a sharp increase on the amount of data that is transmitted across wireless networks. Such networks are, in general, error-prone and require, from the image coding system, techniques to protect the data, detect errors and recover from them. In fact, even if at the network level the protocols provide for error protection and concealment, either there is a non-negligible residual error or the overhead of such protections is too large. JPEG and JPEG-LS provide
Supported functionality
Besides compression efficiency and complexity, many applications benefit or even require other features which may determine the choice of a particular algorithm for an application. These extra features are often referred to as the set of supported functionality. Examples of such features are the ability to distribute quality in a non-uniform fashion across the image (e.g. ROI), resilience to residual transmission errors, random access to different regions of the coded image, scalability of the
Conclusions
This work aims at providing a comparative evaluation and assessment of JPEG 2000 performance, from various points of view, such as compression efficiency, complexity and set of supported functionality. The efficiency of various features that can be expected from a number of recent as well as most popular still image coding algorithms have been studied and compared. To do so, many aspects have been considered including genericity of the algorithm to code different types of data in lossless and
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