compression

Hyperspectral images due to simultaneous acquisition of data in more than hundreds narrow and close spectral bands, have a very high correlation bandwidth. Hence, in order to store in less storage space, higher transmission speed and less bandwidth, they need compression. Various lossless and lossy methods for compression are exist, that can be in the spatial domain or in the spectrum domain. But, regard to the importance of spectral information of hyperspectral images in remote sensing, this compression should be done by this condition that the spectral information of this kind of images is well preserved. Compression methods can be based on either the predictive function or using of a codebook, to compress information. Data compression can also be done based on transformation coding, which these transformations can be cosine functions (DCTs), wavelet functions (DWTs), or principal component analysis (PCAs). Of course, PCA-based compression is one of the most effective ways to eliminate image correlations and reduce their volume. Another extension is the method of using curve fitting, which is applied exclusively to compress hyperspectral images due to its effect on the image spectrum. This method uses the spectral signature of the each pixel of image to reduce the feature by finding the closest approximation function to express the curve and storing its coefficients as a new feature for reconstruction compressed data. By replacing these coefficients in the equation of approximation, spectrum reflection curve for each pixel can be reconstructed. This method has very good results in comparison with previous methods such as PCA, but in compression using this method, the SRC curve has been approximated in some points with distortion. In this paper, we tried to eliminate these distortions, by finding points which have distortion and Breakdown the SCR. On the other hand, by using the Savitsky-Golay smoothing filter we can also reduce distortion and increase the PSNR. Another way to eliminate or reduce this distortion described in this article is as follow: At the first the spectral signature of each pixel of the intended data is smoothed by a Savitsky-Golay smoothing filter and then by using a particular method is divided into adjoining adjacent spaces and then a curve is plotted for each slice of data. By choosing the best degree and window length for smoothing and selecting the best degree of numerator and denominator of function, the coefficients of the selected rational function are considered as new features of the image. By using the proposed method, in addition to eliminating the distortion, the PSNR level is became much higher and the reconstructed image quality is very close to the original image.

This paper investigates the problem of image steganography. The existing algorithms provide low performance due to disregarding the structure and properties of images. Furthermore, most of these schemes are lossy. Considering the shortcomings of the previous algorithms, we propose an effective algorithm, namely Lossless and High Capacity Image Steganography (LHCIS), which embeds the secret image into the cover image losslessly. The proposed scheme comprises secret image compression and embedding the compressed image into the cover image using the LSB algorithm. The compression procedure includes two phases. The first phase constructs the corresponding difference image to the given secret image. The mentioned image represents the variations of the adjacent pixels of the secret one. Each pixel of this image is derived by subtracting its corresponding pixel in the secret image from the adjacent one. The difference image is encoded to reduce its storage space in the second phase. The compression ratio is high due to the small values of the pixels of the difference image. According to the performed simulations, LHCIS increases the embedding capacity by 40%. Furthermore, it improves the PSNR of the stego image by 2.16 in comparison to the previous algorithms.

Power consumption of mobile screens of laptops, tablets and mobile phones, due to their limited battery capacity, is one of the problems that users of these devices face. Some new hardware technologies, such as dynamic voltage and frequency scaling (DVFS), have reduced the consumption of these displays in some cases, but there are still many problems with DVFS. The High Efficiency Video Coding (HEVC) Standard is the latest and most useful video compression method used for high quality video capture and display in formats such as 4K and 8K. With all the advantages of HEVC, one of its disadvantages is the high power consumption when encrypting and decrypting videos, which should be minimized by providing solutions. In this paper, the latest methods in reducing power consumption in mobile monitors are reviewed and compared, and suggestions for its improvement are presented. Reducing power consumption in these methods sometimes reaches to 20 nj/px for video formats.

This research seeks to promote one of the widely being used algorithms in machine learning, known as the random forest algorithm. For this purpose, we use compression and parallelization techniques. The main challenge we address in this research is about application of the random forest algorithm in processing and analyzing big data. In such cases, this algorithm does not show the usual and required performance, due to the needed large number of memory access. This research demonstrates how we can achieve the desired goal by using an innovative compression method, along with parallelization techniques. In this regard, the same components of the trees in the random forest are combined and shared. Also, a vectorization-based parallelization approach, along with a shared-memory-based parallelization method, are used in the processing phase. In order to evaluate its performance, we run it on the Kaggle benchmarks, which are being used widely in machine learning competitions. The experimental results show that contribution of the proposed compression method, could reduce 61% of the required processing time; meanwhile, application of the compression along with the named parallelization methods could lead to about 95% of improvement. Overall, this research implies that the proposed solution can provide an effective step toward high performance computing.

The analysis of gene sequences is fundamentally important for exploring biological functions. Recently, the cost of gene sequencing has dropped sharply, thereby resulting in the production of considerable genomic data. However, the costs of saving, processing, and transferring these data are rising. At present, processing this massive volume of information is done by character based method which is highly time - consuming. Alternative methods challenge these problems in the realm of signal processing. Accordingly, the signal outlook to the genome, signal processing of the genome and compression of the genome are presently hot issues which are practically in demand. Compression reduces the cost, memory space, bandwidth for exchange, and the time required for analysis. In this study, the character genes were firstly represented as signals. Then, these genomic signals were compressed by compressed sensing. Consequently, they were reconstructed by bayesian learning method. Adopted criteria for reconstruction were PRD and NMSE, respectively. Then, signals were selected with a compression rate of 75% for comparison. Meanwhile, the same cluster analysis was run with character based method. The results indicated that the time needed for signal based method was considerably lower than the character based method.