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PubMed2006

Perceptual image hashing via feature points: performance evaluation and tradeoffs.

We propose an image hashing paradigm using visually significant feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to introduce randomness, which, in turn, reduces vulnerability to adversarial attacks. The proposed hash algorithm withstands standard benchmark (e.g., Stirmark) attacks, including compression, geometric distortions of scaling and small-angle rotation, and common signal-processing operations. Content changing (malicious) manipulations of image data are also accurately detected. Detailed statistical analysis in the form of receiver operating characteristic (ROC) curves is presented and reveals the success of the proposed scheme in achieving perceptual robustness while avoiding misclassification.

Algorithms↗
PubMed1989

Edema formation in spinal nerve roots induced by experimental, graded compression. An experimental study on the pig cauda equina with special reference to differences in effects between rapid and slow onset of compression.

Edema formation in spinal nerve roots of the pig cauda equina was studied following experimental compression at various pressure levels, durations, and rates of onset, using a fluorescence microscopic technique. The time-pressure thresholds for the occurrence of edema in the nerve roots were: following rapid onset of compression (0.05-0.1 seconds), 2 minutes at both 50 mm Hg and 200 mm Hg, and following slow onset of compression (the pressure was slowly increased during 15-20 seconds), 2 hours at 50 mm Hg and 2 minutes at 200 mm Hg. Generally, the edema formation was more pronounced after rapid than after slow onset of compression. The data in this study also indicate that intraneural edema might be more easily formed in nerve roots than in peripheral nerves after compression injury.

Animals↗
PubMed2005

The effect of audibility, signal-to-noise ratio, and temporal speech cues on the benefit from fast-acting compression in modulated noise.

The objective of the experiment was to investigate three aspects that might contribute to the benefit of fast-acting compression seen in normal-hearing listeners. Six normal-hearing listeners were tested with speech recognition in a fully modulated noise (FUM) either through a fast-acting compressor or through linear amplification. In the first experiment, three different presentation levels of the FUM noise (15, 30, and 45 dB SL) were tested. The second experiment manipulated the control signal of the compressor independently of the audio input signal at four signal-to-noise ratios (-15, 10, -5, and 0 dB). A signal correlated noise version of the speech signal was tested in the third experiment at three speech-to-noise ratios (-20, -15 and -10 dB). Results showed that performance was better with compression than with linear amplification through all of the tested conditions at least when the signal-to-noise ratio was negative. The results suggest that other aspects of the hearing impairment than those simulated here are involved in the degraded performance seen for some hearing-impaired listeners with fast-acting compression.

Acoustic Stimulation↗
PubMed2006

A flexible structure for fully scalable motion-compensated 3-D DWT with emphasis on the impact of spatial scalability.

We investigate the implications of the conventional "t+2-D" motion-compensated (MC) three-dimensional (3-D) discrete wavelet/subband transform structure for spatial scalability and propose a novel flexible structure for fully scalable video compression. In this structure, any number of levels of "pretemporal" spatial wavelet decomposition are performed on the original full resolution frames, followed by MC temporal decomposition of the subbands within each spatial resolution level. Further levels of "posttemporal" spatial decomposition may be performed on the spatiotemporal subbands to provide additional levels of spatial scalability and energy compaction. This structure allows us to trade energy compaction against the potential for artifacts at reduced spatial resolutions. More importantly, the structure permits extensive study of the interaction between spatial aliasing, scalability and energy compaction. We show that where the motion model fails, the "t+2-D" structure inevitably produces misaligned spatial aliasing artifacts in reduced resolution sequences. These artifacts can be removed by using pretemporal spatial decomposition. On the other hand, we also show that the "t+2-D" structure necessarily maximizes compression efficiency. We propose different schemes to minimize the loss of compression efficiency associated with pretemporal spatial decomposition.

Algorithms↗
PubMed2005

Principles of digital dynamic-range compression.

This article provides an overview of dynamic-range compression in digital hearing aids. Digital technology is becoming increasingly common in hearing aids, particularly because of the processing flexibility it offers and the opportunity to create more-effective devices. The focus of the paper is on the algorithms used to build digital compression systems. Of the various approaches that can be used to design a digital hearing aid, this paper considers broadband compression, multi-channel filter banks, a frequency-domain compressor using the FFT, the side-branch design that separates the filtering operation from the frequency analysis, and the frequency-warped version of the side-branch approach that modifies the analysis frequency spacing to more closely match auditory perception. Examples of the compressor frequency resolution, group delay, and compression behavior are provided for the different design approaches.

Algorithms↗
PubMed2005

HVS-based medical image compression.

INTRODUCTION: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. METHODS: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. RESULTS: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. CONCLUSIONS: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.

Algorithms↗
PubMed2005

Sparse geometric image representations with bandelets.

This paper introduces a new class of bases, called bandelet bases, which decompose the image along multiscale vectors that are elongated in the direction of a geometric flow. This geometric flow indicates directions in which the image gray levels have regular variations. The image decomposition in a bandelet basis is implemented with a fast subband-filtering algorithm. Bandelet bases lead to optimal approximation rates for geometrically regular images. For image compression and noise removal applications, the geometric flow is optimized with fast algorithms so that the resulting bandelet basis produces minimum distortion. Comparisons are made with wavelet image compression and noise-removal algorithms.

Algorithms↗
PubMed2006

Nonlinear image representation for efficient perceptual coding.

Image compression systems commonly operate by transforming the input signal into a new representation whose elements are independently quantized. The success of such a system depends on two properties of the representation. First, the coding rate is minimized only if the elements of the representation are statistically independent. Second, the perceived coding distortion is minimized only if the errors in a reconstructed image arising from quantization of the different elements of the representation are perceptually independent. We argue that linear transforms cannot achieve either of these goals and propose, instead, an adaptive nonlinear image representation in which each coefficient of a linear transform is divided by a weighted sum of coefficient amplitudes in a generalized neighborhood. We then show that the divisive operation greatly reduces both the statistical and the perceptual redundancy amongst representation elements. We develop an efficient method of inverting this transformation, and we demonstrate through simulations that the dual reduction in dependency can greatly improve the visual quality of compressed images.

Algorithms↗
PubMed1994

[The compression of numerical radiological images].

A digital radiological image is made by a number of pixels, each of them characterized by a definite numerical value obtained by quantization which represents the luminance in that specific image unit. Spatial resolution and dynamic range are the main factors in determining the quality of a digital radiological image. The product of the two above factors defines the global dimensions of the image and is expressed in bits. In conventional radiographic images the global dimension of the image is expressed in MBytes, because of its high spatial and contrast resolution. To reduce the visualization and storage requirements as well as the transmission time of large image data sets, compression algorithms have been recently introduced. These algorithms are based on the fact that often in a digital image parts of the binary data are "redundant", that is they are not necessary for correct image representation. Therefore, compression methods are aimed at reducing both statistical and perceptive redundancy. Statistical redundancy is reduced by means of lossless coding which does not allow to compress images with a ratio higher than 4-5:1 and that--by definition--allows to recover the original image quality. On the other hand "lossy" compression algorithms, which eliminate the perceptive redundancy, are based on the reduction of spatial resolution and dynamic range and on transform-based methods. In particular, the latter have usually been more successful in terms of efficient compression, even when applied to conventional radiographic images. The basic transform procedure can be modified at various levels. JPEG is one of these methods, originally developed for photographic images, which can be usefully applied to radiological images as well. Lossy procedures allow to reach higher compression ratios than lossless methods, but the decrease in information content must be prevented from reducing diagnostic accuracy. In order to assess the diagnostic efficiency of the images compressed with lossy methods, semi-objective analyses are usually performed and ROC curves are produced and evaluated. A model ROC analysis is presented.

Algorithms↗