A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.

Super-resolution image reconstruction: a technical overview

One of the aims of the standardization committee has been the development of Part I, which could be used on a royalty- and fee-free basis. This is important for the standard to become widely accepted. The standardization process, which is coordinated by the JTCI/SC29/WG1 of the ISO/IEC has already produced the international standard (IS) for Part I. In this article the structure of Part I of the JPFG 2000 standard is presented and performance comparisons with established standards are reported. This article is intended to serve as a tutorial for the JPEG 2000 standard. The main application areas and their requirements are given. The architecture of the standard follows with the description of the tiling, multicomponent transformations, wavelet transforms, quantization and entropy coding. Some of the most significant features of the standard are presented, such as region-of-interest coding, scalability, visual weighting, error resilience and file format aspects. Finally, some comparative results are reported and the future parts of the standard are discussed.

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The JPEG 2000 still image compression standard

Reversible watermarking enables the embedding of useful information in a host signal without any loss of host information. Tian's difference-expansion technique is a high-capacity, reversible method for data embedding. However, the method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. We propose a histogram shifting technique as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. We also propose a reversible data-embedding technique called prediction-error expansion. This new technique better exploits the correlation inherent in the neighborhood of a pixel than the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to form an effective method for data embedding. The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities

Expansion Embedding Techniques for Reversible Watermarking

Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent blind detection of a public watermark. In this paper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a one-dimensional (1-D) signal obtained by taking the Fourier transform of the image, resampling the Fourier magnitudes into log-polar coordinates, and then summing a function of those magnitudes along the log-radius axis. Rotation of the image results in a cyclical shift of the extracted signal. Scaling of the image results in amplification of the extracted signal, and translation of the image has no effect on the extracted signal. We can therefore compensate for rotation with a simple search, and compensate for scaling by using the correlation coefficient as the detection measure. False positive results on a database of 10 000 images are reported. Robustness results on a database of 2000 images are described. It is shown that the watermark is robust to rotation, scale, and translation. In addition, we describe tests examining the watermarks resistance to cropping and JPEG compression.

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Rotation, scale, and translation resilient watermarking for images

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this paper, the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain; 2) RDH into image compressed domain (e.g., JPEG); 3) RDH suitable for image semi-fragile authentication; 4) RDH with image contrast enhancement; 5) RDH into encrypted images, which is expected to have wide application in the cloud computation; and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

Reversible Data Hiding: Advances in the Past Two Decades

The Joint Bi-Level Image Experts Group (JBIG), an international study group affiliated with ISO/IEC and ITU-T, is in the process of drafting a new standard for lossy and lossless compression of bilevel images. The new standard, informally referred to as JBIG2, will support model-based coding for text and halftones to permit compression ratios up to three times those of existing standards for lossless compression. JBIG2 will also permit lossy preprocessing without specifying how it is to be done, In this case, compression ratios up to eight times those of existing standards may be obtained with imperceptible loss of quality. It is expected that JBIG2 will become an international standard by 2000.

The emerging JBIG2 standard

Summary form only given. We present improvements to a general type of lossless, lossy, and refinement coding of bi-level images (Martins and Forchhammer, 1996). Loss is introduced by flipping pixels. The pixels are coded using arithmetic coding of conditional probabilities obtained using a template as is known from JBIG and proposed in JBIG-2 (Martins and Forchhammer). Our new state-of-the-art results are obtained using the more general free tree instead of a template. Also we introduce multiple refinement template coding. The lossy algorithm is analogous to the greedy 'rate-distortion'-algorithm of Martins and Forchhammer but is based on the free tree.

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Lossy/lossless coding of bi-level images

Presently, sequential tree coders are the best general purpose bilevel image coders and the best coders of halftoned images. The current ISO standard, Joint Bilevel Image Experts Group (JBIG), is a good example. A sequential tree coder encodes the data by feeding estimates of conditional probabilities to an arithmetic coder. The conditional probabilities are estimated from co-occurrence statistics of past pixels, the statistics are stored in a tree. By organizing the code length calculations properly, a vast number of possible models (trees) reflecting different pixel orderings can be investigated within reasonable time prior to generating the code. A number of general-purpose coders are constructed according to this principle. Rissanen's (1989) one-pass algorithm, context, is presented in two modified versions. The baseline is proven to be a universal coder. The faster version, which is one order of magnitude slower than JBIG, obtains excellent and highly robust compression performance. A multipass free tree coding scheme produces superior compression results for all test images. A multipass free template coding scheme produces significantly better results than JBIG for difficult images such as halftones. By utilizing randomized subsampling in the template selection, the speed becomes acceptable for practical image coding.

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Tree coding of bilevel images

Summary form only given. We investigate lossless coding of video using predictive coding and motion compensation. The new coding methods combine state-of-the-art lossless techniques as JPEG (context based prediction and bias cancellation, Golomb coding), with high resolution motion field estimation, 3D predictors, prediction using one or multiple (k) previous images, predictor dependent error modelling, and selection of motion field by code length. We treat the problem of precision of the motion field as one of choosing among a number of predictors. This way, we can incorporate 3D-predictors and intra-frame predictors as well. As proposed by Ribas-Corbera (see PhD thesis, University of Michigan, 1996), we use bi-linear interpolation in order to achieve sub-pixel precision of the motion field. Using more reference images is another way of achieving higher accuracy of the match. The motion information is coded with the same algorithm as is used for the data. For slow pan or slow zoom sequences, coding methods that use multiple previous images perform up to 20% better than motion compensation using a single previous image and up to 40% better than coding that does not utilize motion compensation.

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Lossless compression of video using motion compensation

Presently, tree coders are the best bi-level image coders. The current ISO standard, JBIG, is a good example. By organising code length calculations properly a vast number of possible models (trees) can be investigated within reasonable time prior to generating code. Three general-purpose coders are constructed by this principle. A multi-pass free tree coding scheme produces superior compression results for all test images. A multi-pass fast free template coding scheme produces much better results than JBIG for difficult images, such as halftonings. Rissanen's algorithm 'Context' is presented in a new version that without sacrificing speed brings it close to the multi-pass coders in compression performance.

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Bo Martins

Papers

The emerging JBIG2 standard

Lossy/lossless coding of bi-level images

Tree coding of bilevel images

Lossless compression of video using motion compensation

Bi-level image compression with tree coding