Jarno Mielikainen

Bio: Jarno Mielikainen is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Lossless compression & Data compression. The author has an hindex of 19, co-authored 53 publications receiving 1855 citations. Previous affiliations of Jarno Mielikainen include Yonsei University & Lappeenranta University of Technology.

LSB matching revisited

Abstract: This letter proposes a modification to the least-significant-bit (LSB) matching, a steganographic method for embedding message bits into a still image. In the LSB matching, the choice of whether to add or subtract one from the cover image pixel is random. The new method uses the choice to set a binary function of two cover pixels to the desired value. The embedding is performed using a pair of pixels as a unit, where the LSB of the first pixel carries one bit of information, and a function of the two pixel values carries another bit of information. Therefore, the modified method allows embedding the same payload as LSB matching but with fewer changes to the cover image. The experimental results of the proposed method show better performance than traditional LSB matching in terms of distortion and resistance against existing steganalysis.

Clustered DPCM for the lossless compression of hyperspectral images

Abstract: A clustered differential pulse code modulation lossless compression method for hyperspectral images is presented. The spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for each cluster. The achieved compression ratios presented here are compared with those of existing methods. The results show that the proposed lossless compression method for hyperspectral images outperforms previous methods.

Lossless compression of hyperspectral images using lookup tables

Abstract: In this letter, we propose a new algorithm for lossless compression of hyperspectral images. The proposed method searches the previous band for a pixel of equal value to the pixel co-located to the one to be coded. The pixel in the same position as the obtained pixel in the current band is used as the predictor. Lookup tables are used to speed up the search. The algorithm is suitable for compression of hyperspectral image data in the band-interleaved-by-line (BIL) format. The method outperforms other state-of-the-art compression algorithms for the BIL data, at a lower time complexity level. Moreover, its compression ratios for the band sequential format data are within a few percentage points of the current state-of-the-art methods.

Lossless Compression of Hyperspectral Images Using a Quantized Index to Lookup Tables

Abstract: We propose an enhancement to the algorithm for lossless compression of hyperspectral images using lookup tables (LUTs). The original LUT method searched the previous band for a pixel of equal value to the pixel colocalized with the one to be predicted. The pixel in the same position as the obtained pixel in the current band is used as a predictor. LUTs were used to speed up the search. The LUT method has also been extended into a method called Locally Averaged Interband Scaling (LAIS)-LUT that uses two LUTs per band. One of the two LUT predictors that is the closest one to the LAIS estimate is chosen as the predictor for the current pixel. We propose the uniform quantization of the colocated pixels before using them for indexing the LUTs. The use of quantization reduces the size of the LUTs by an order of magnitude. The results show that the proposed method outperforms previous methods; a 3% increase in compression efficiency was observed compared to the current state-of-the-art method, LAIS-LUT.

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Abstract: Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Using the triangle inequality to accelerate k-means

Abstract: The k-means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids unnecessary distance calculations by applying the triangle inequality in two different ways, and by keeping track of lower and upper bounds for distances between points and centers. Experiments show that the new algorithm is effective for datasets with up to 1000 dimensions, and becomes more and more effective as the number k of clusters increases. For k ≥ 20 it is many times faster than the best previously known accelerated k-means method.

The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions

Abstract: Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a s...

Efficient Steganographic Embedding by Exploiting Modification Direction

Abstract: A novel method of steganographic embedding in digital images is described, in which each secret digit in a (2n+1)-ary notational system is carried by n cover pixels and, at most, only one pixel is increased or decreased by 1. In other words, the (2n+1) different ways of modification to the cover pixels correspond to (2n+1) possible values of a secret digit. Because the directions of' modification are fully exploited, the proposed method provides high embedding efficiency that is better than previous techniques