Fast fractal-image compression using feature vector matching

A novel fractal image coding algorithm based on domain blocks sorting strategies and modified no search scheme is proposed in this paper. On one hand, in order to improve the encoding time, a modified no search (MNS) scheme is adopted. Firstly, the image is divided into blocks of different size utilizing an adaptive quadtree partition method. Secondly, one finds the location of the best matching domain block using the MNS scheme for the range blocks, whose sizes are larger than the preset minimum value. Thirdly, the types of the range block and domain block are computed employing the proposed approach, and then the corresponding computation of mean square error (MSE) is determined. The computation of the MSE is reduced and the encoding phase speeds up. On the other hand, the range blocks with the minimal sizes are encoded applying the proposed domain blocks sorting (DBS) method. Contrast experiment results show that the proposed algorithm can obtain good quality of the reconstructed images and shorten the encoding time significantly.

In order to achieve fast computational speed with good visual quality of output video, we propose a frequency domain based new fractal video compression scheme. Normalized cross correlation is used to find the structural self similar domain block for the input range block. To increase the searching speed, cross correlation is implemented in the frequency domain using FFT with one computational operation for all the domain blocks instead of individual block wise calculations. The encoding time is further minimized by applying rotation and reflection DFT properties to the IFFT of zero padded range blocks. The energy of overlap small size domain blocks is pre-computed for the entire reference frame and retaining the energies of the overlapped search window portion of previous adjacent block. Quadtree decompositions are obtained by using domain block motion compensated prediction error as a threshold to control the further partitions of the block. It provides a better level of adaption to the scene contents than fixed block size approach. The result shows that, on average, the proposed method can raise the encoding speed by 48.8 % and 90 % higher than NHEXS and CPM/NCIM algorithms respectively. The compression ratio and PSNR of the proposed method is increased by 15.41 and 0.89 dB higher than that of NHEXS on average. For low bit rate videos, the proposed algorithm achieve the high compression ratio above 120 with more than 31 dB PSNR.

Fractal coding offers many promising qualities, such as high compression ratio, good image quality, and resolution independence of the decoded image. However the encoding process suffers from the long search time of the domain block pool. Much research has been done to speed it. The fractal theory, and many speed-up techniques, such as block classification, code-book clustering, etc. are discussed in the recent book by Y. Fisher (1995). We present a different scheme to speed up the search. We devise an incremental procedure which limits the domain block pixels, given a range block and an upper bound on their distance. We arrange the domain blocks in a tree, and navigate it to select a small number of candidate blocks. Experiments show that the encoding process speeds up by up-to 50 times. Our procedure works in conjunction with other methods, such as block classification. (3 pages)

The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.

Quantitative research on thermodynamic process and efficiency of a LNG heavy-duty engine with high compression ratio and hydrogen enrichment

An experimental investigation was carried out on a computer interfaced single cylinder variable compression ratio, compression ignition engine to optimize the performance characteristics and to find the useful higher compression ratio (UHCR) with hydrogen-diesel dual fuel mode. Experimentations were conducted on five different compression ratios and the performance characteristics were calculated. The effect of blending on NOx, HC, CO, and particulate matter were measured and reported. The rate of heat release and speed of combustion with increase in compression ratio with simultaneous increase in hydrogen substitution were measured. Intake temperature of air was increased and for three different temperatures, the effect of increase in temperature of air-hydrogen mixture on NOx were studied and found that there was a sharp increase in the NOx value as the inlet temperature was increased from 65 to 85°C.

The present study attempted to study perception of time-compressed speech and the effect of compression ratio for phonemically balanced (PB) word lists in Kannada and monosyllables. The test was administered on 30 normal hearing individuals at compression ratios of 40%, 50%, 60%, 70% and 80% for PB words in Kannada and monosyllables.The results of the study showed that the speech identification scores for time-compressed speech reduced with increase in compression ratio. The scores were better for monosyllables compared to PB words especially at higher compression ratios. The study provides speech identification scores at different compression ratio for PB words and monosyllables in individuals with normal hearing. The results of the study also showed that the scores did not vary across gender for all the compression ratios for both the stimuli. The same test material needs to be compared the clinical population with central auditory processing disorder for clinical validation of the present results.

Fractal image compression (FIC) is a very popular technique in image compression applications due to its simplicity and superior performance. However, it has a major drawback, which is the long encoding time. This is due to the requirement of performing huge similarity search for encoding each small portion in the image. Thus, reducing the search time of FIC while keeping the quality of reconstructed images at acceptable level is still an active research topic. Therefore, this paper has focused on studying the search problem of the conventional full-search FIC algorithm and the impact of employing a spatial dynamic search technique instead with the matching threshold strategy. Unlike the conventional full-search method that is a spatially static where the search starts from a fixed position (normally from the top-left corner of the image to the bottom-right corner) regardless of the position of the range block being encoding, the idea of the dynamic search method is simple, but effective, and it is based on starting the search from the closest domain block to the range block that needs to be encoded. These two search schemes are tested under different matching threshold values, in which the search is terminated whenever a domain block with an acceptable matching level is found. To make the study comprehensive, the test is performed for different image sizes and types, range block and partitioning step sizes, and quantization levels. The experimental results show the significant impact of using the dynamic search method instead of the conventional search method specifically when the threshold is large. For the best encoding parameters, the improvement amount that can be achieved is near to 90 % in terms of search reduction and 1.6 dB PSNR in terms of image quality.

This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade‐off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.

Since the early 20th century, most ground vehicles are driven with gasoline and diesel. The degradation of the environment affects human on earth unless the quality of the air is improved. One of the alternative fuels, LPG, is potentially capable of lowering vehicular emissions when compared to gasoline or diesel. There is a penalty in power output resulting from the use of LPG because the engine can induce less amount of air with Mixer system comparing with gasoline engine. Currently, the liquid-phase LPG is injected into the intake port of the engine, the fuel vaporizes enroute to the combustion chamber. Therefore, the performance and combustion processes of the tested engine are investigated with different LPG fuel systems. The test engine was developed and named heavy-duty VACRE. The test engine for this work operates 1400rpm with MBT conditions. The major conclusions of the work include; 1) The power output of LPi system with liquid-phase is approximately 17% higher than that of vapor-phase Mixer system due to increases of volumetric efficiency. And the MBT spark timing of LPi system is approximately 25% more advanced than that of Mixer system at λ value 1.0; 2) The LPi system shows both the maximum heat release rate and the cumulative heat release to be approximately 20% higher than the Mixer system; 3) Maximum cylinder pressure decrease with increase of compression ratio and a point of maximum cylinder pressure is delayed with high compression ratio.

Previous studies have shown that increase compression ratio (CR) is an effective way to improve thermal efficiency of gasoline engine without changing the mechanical structure and working cycle, however, it is limited by engine knock when increasing the intake boosting under high load operation. This study aimed to solve the knock problem of gasoline engine with higher CR by application of Miller cycle, which can be implemented by either early or late intake valve closing (EIVC or LIVC). Therefore, in this paper, based on the engine with CR of 13.5 and electromagnetic valves train (EMVT), a comparative study was carried out to investigate the effects of EIVC and LIVC on engine performance, by theoretical modeling and calculation. The results show that, at high load, EIVC strategy is more preferred than LIVC owing to its lower total power consumption, which can improve the indicated mean effective pressure (IMEP) by 0.0371 bar, while enhance turbulence intensity and improve combustion. And at part load, the advantage for EIVC declines gradually, nevertheless, it can still sensitively adjust the EGR rate and thus reduce NOx. This results of quantitative analysis about two Miller cycles can provide valuable reference for engine designers and researchers.

Effect of engine compression ratio, injection timing, and exhaust gas recirculation on gaseous and particle number emissions in a light-duty diesel engine

Data compression has become an important issue in relation to storage and transmission. This issue is especially true for databases consisting of a large number of detailed computer images. Many methods have been proposed in recent years for achieving high compression ratios for compressed image storage. A very promising compression technique, in terms of compression ratios, is fractal image compression. Fractal image compression exploits natural affine redundancy present in typical images to achieve a high compression ratio in a lossy compression format. Fractal based compression algorithms, however, have high computational demands. To obtain faster compression, a sequential fractal image compression algorithm may be translated into a parallel algorithm. This translation takes advantage of the inherently parallel nature, from a data domain viewpoint, of the fractal transform process. >

Sketch based coding of grey level images

In the scientific domain, extremely large amounts of data are generated by large-scale high performance computing (HPC) simulations. Storing and sending such vast volumes of data poses serious scalability and performance issues, which can be considerably mitigated by data compression techniques which significantly reduced storage size and data movement burdens. Since scientific data are being shared by scientists more and more frequently, data security methods that ensure the confidentiality, integrity, and availability of data itself are becoming increasingly important. As such, combing compression and encryption is critical to storing large-scale datasets securely. In this work, we explore how to integrate data compression and cryptography techniques as efficiently as possible for big scientific datasets in the HPC field. We perform thorough experiments using different scientific datasets with the state-of-the-art error-bounded lossy compressor - SZ - on a real-world supercomputing environment. Experiments verify that performing encryption before lossy compression (a.k.a., encr-cmpr method) may invalidate the advantage of compression algorithms. By contrast, executing encryption after lossy compression (a.k.a., cmpr-encr method) keeps not only high compression ratios but high overall execution speed. Experiments also verify that the encryption overhead under the cmpr-encr method decreases with increasing compression ratios, which means very good scalability.