Lossy compression of DNA microarray images

Objectives. The widespread use of systems for capturing light fields is due to the high quality of the reproduced image. This type of capture, although qualitatively superior to traditional methods to capturing volumetric images, generates a huge amount of data needed to reconstruct the original captured 4D light field. The purpose of the work is to consider traditional and extended to four-dimensional image compression algorithms, to perform a comparative analysis and determine the most suitable.Methods. Mathematical methods of signal processing and methods of statistical analysis are used.Results. Algorithms are compared and analyzed in relation to the compression of four-dimensional light fields using the PSNR metric. The selected evaluation criterion is affected not only by the dimension of the compression algorithm, but also by the distance of the baseline of the capture setting, since the difference between images increases with the distance between the optical centers of each camera matrix. Thus, for installations consisting of an array of machine vision cameras located on racks and placed in a room, the obvious choice would be to use conventional image compression methods. Furthermore, based on the assessment of the arbitrariness of video compression methods, it should be noted that the XVC algorithm remains undervalued, although its results are higher. Algorithm AV1 can be considered the next in order of importance. It has been established that the latest compression algorithms show higher performance if compared to their predecessors. It has also been shown that with a small distance between the optical centers of the captured images, the use of video compression algorithms is preferable to the use of image compression algorithms, since they show better results in both three-dimensional and four-dimensional versions.Conclusions. A comparison of the results obtained shows the need to use algorithms from the video compression family (XVC, AV1) on installations with a long baseline (mounted on camera stands). When working with integrated light field cameras (Lytro) and setting the capture with a short baseline, it is recommended to use image compression algorithms (JPEG). In general, video compression algorithms are recommended, in particular XVC, since on average it shows an acceptable level of PSNR in both the case of a short and long installation baseline.

In machine learning problems, there are two main algorithms: supervised learning and unsupervised learning. Supervised learning algorithms can be used to classify data for tagged data; non-supervised learning algorithms can be used to cluster data for unlabeled data. This paper discusses the basic principles of clustering algorithm and selection of key parameters of clustering algorithm. The application of clustering algorithm in image compression is also analyzed. This paper also emphasizes the problems that should be paid attention to when using clustering. Finally, a practical case of image compression with K-means is given.

Focused on the image data storage, compression and transmission of mars rover, system design and key hardware/software architecture design are carried out in this paper based on the analysis of function requirements and key design features. Image compression algorithm and transmission protocol are proposed, and the image storage and compression module in Mars rover is designed and implemented. The multi-camera partition parallel image file system based on FPGA（Field Programmable Gate Array）is realized. The adaptive first 1 run length coding algorithm for image compression based on DSP is realized on this module. Finally the function of multi-camera image data storage and management, flexible control of image compression ratio, quality progressive transmission, coding of region of interest, anti-error code diffusion and image thumbnail downward transmission are realized in the engineering development, meeting the reliable, efficient and flexible application requirements of China’s first Mars exploration mission.

Many image and video compression algorithms work by splitting the image into blocks and producing variable-length code bits for each block data. If variable-length code data are transmitted consecutively over error-prone channel without any error protection technique, the receiving decoder cannot decode the stream properly. So the standard image and video compression algorithms insert some redundant information into the stream to provide some protection against channel errors. One of such redundancy is resynchronization marker, which enables the decoder to restart the decoding process from a known state in the event of transmission errors, but its frequent use should be restricted not to consume bandwidth too much. The Error Resilient Entropy Code (EREC) is well known method which can regain synchronization without any redundant information. It can work with the overall prefix codes, which many image compression methods use. This paper proposes an improvement to FEREC (Fast Error-Resilient Entropy Coding). It first calculates initial searching position according to bit lengths of consecutive blocks. Second, initial offset is decided using statistical distribution of long and short blocks, and initial offset is adjusted to insure all possible offset value can be examined. The proposed algorithm can speed up the construction of EREC slots, and can preserve compressed image quality in the event of transmission errors. The simulation result shows that the quality of transmitted image is enhanced about 0.3--3.5 dB compared with the existing FEREC when random channel error happens.

To prolong the life cycle of wireless sensor network, the basic theory of wavelet transform and its application in image compression are described, and several classic image compression algorithms based on wavelet transform are studied in depth. A compression algorithm combining the improved wavelet transform and Set Partitioning in Hierarchical Trees (SPIHT) algorithm of hierarchical wavelet tree set segmentation is proposed to effectively balance the energy consumption of each node in the sensor network and prolong the life of the whole wireless sensor network and an improved distributed image compression and transmission algorithm is proposed based on the distributed multi-node cooperative processing algorithm based on wavelet transformation, and detailed analytical test and energy consumption simulation experiment are carried out to verify the feasibility of the algorithm. The results show that the platform effectively implements and verifies the proposed algorithm, which can effectively realize the compression and transmission of distributed images, equalize the energy consumption of each sensor node in the network, and has strong practicability.

A multidimensional incremental parsing algorithm (MDIP) for multidimensional discrete sources, as a generalization of the Lempel-Ziv coding algorithm, is investigated. It consists of three essential component schemes, maximum decimation matching, hierarchical structure of multidimensional source coding, and dictionary augmentation. As a counterpart of the longest match search in the Lempel-Ziv algorithm, two classes of maximum decimation matching are studied. Also, an underlying behavior of the dictionary augmentation scheme for estimating the source statistics is examined. For an m-dimensional source, m augmentative patches are appended into the dictionary at each coding epoch, thus requiring the transmission of a substantial amount of information to the decoder. The property of the hierarchical structure of the source coding algorithm resolves this issue by successively incorporating lower dimensional coding procedures in the scheme. In regard to universal lossy source coders, we propose two distortion functions, the local average distortion and the local minimax distortion with a set of threshold levels for each source symbol. For performance evaluation, we implemented three image compression algorithms based upon the MDIP; one is lossless and the others are lossy. The lossless image compression algorithm does not perform better than the Lempel-Ziv-Welch coding, but experimentally shows efficiency in capturing the source structure. The two lossy image compression algorithms are implemented using the two distortion functions, respectively. The algorithm based on the local average distortion is efficient at minimizing the signal distortion, but the images by the one with the local minimax distortion have a good perceptual fidelity among other compression algorithms. Our insights inspire future research on feature extraction of multidimensional discrete sources.

Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark. However, they are slow to train (due to backprop-through-time) and, to the best of our knowledge, have not been systematically evaluated on a large variety of datasets. In this paper, we perform the first large scale comparison of recent state-of-the-art hybrid neural compression algorithms, while exploring the effects alternative training strategies (when applicable). The hybrid recurrent neural decoder is a former state-of-the-art model (recently overtaken by a Google model) that can be trained using backprop-through-time (BPTT) or with alternative algorithms like sparse attentive backtracking (SAB), unbiased online recurrent optimization (UORO), and real time recurrent learning (RTRL). We compare these training alternatives along with the Google models (GOOG and E2E) on 6 benchmark datasets. Surprisingly, we found that the model trained with SAB performs the better (outperforming even BPTT), resulting in faster convergence and better peak signal-to-noise ratio.

Research at the Space Dynamics Laboratory(SDL) / Utah State University in image compression has resulted in the development of a hybrid compression algorithm for space related images. The basic algorithm is a hybrid of lossy and lossless compression techniques that have been implemented in hardware [I]. The lossy encoder uses Mean Removed Vector Quantization(MRVQ). The Lossless compressor is an entropy encoder used to encode the errors introduced by the lossy MRVQ. A constant bit rate is maintained by thresholding the MRVQ errors based on the number of previously encoded bits. This paper compares Huffman and Rice Compression techniques to be used in the lossless stage of the hybrid compressor. These method have been selected because hardware to implement them already exist. A single chip implementation of the Rice algorithm is available that processes at the rates required[2].

Microarray image compression: SLOCO and the effect of information loss

Traffic congestion has always been a major problem in the Philippines. This project study developed an integrated sensor system for intelligent transportation, which addressed the lack of automatic traffic monitoring systems to achieve traffic efficiency and safety. The researchers utilized different sensors in gathering data such as water level, temperature, and humidity within a certain area as well as a camera in capturing images and compared the three image compression algorithms, mainly the DCT (Discrete Cosine Transform), DWT (Discrete Wavelet Transform) and SVD (singular value decomposition) in terms of different parameters used in describing and identifying the best type of image compression to be applied prior to transmission to other different nodes. MSE (mean squared error) value, PSNR (Peak signal-to-noise ratio), Compress Ratio, and Process Time were the values of comparison in determining the best compression technique or algorithm for the gathered images. As a result, it was found that DCT characterized the best parameters for image compression. DCT produced the highest PSNR (52.41979dB), the lowest value for MSE (0.37247), and the lowest process time (0.16181s), while SVD was able to produce the most compressed image. This project also utilized solar renewable energy for the power management system, which enabled the system to run independently without any other external power source. This will be beneficial for the community to identify which roads can be used to optimize the mobility of the vehicles and maximize the use of renewable energy; hence will help reduce the traffic congestion issues in the country.

Energy efficient image coding techniques for low power sensor nodes: A review

The NP-Completeness theory states that exact and efficient algorithms are unlikely to exist for the class of NP-difficult problems. One way to deal with NP hardness is to relax the optimality requirement and look for solutions instead that are close to the optimum. This is the main idea behind the approximation algorithms, which are called heuristic or metaheuristic. The problem of motion estimation is a process with a high degree of computational complexity, it requires sufficient memory space and execution time. It represents the cost of static, dynamic and video image sequence coding. The main task is to minimize the distortion rate and improve visual quality. This makes research in the field of coding, image compression and video focus on finding efficient algorithms to carry out the estimation of movement in a reasonable time. If a list of images of n elements is analyzed, there are feasible solutions. So, an exhaustive search is too slow, even for small values of the solution space. Therefore, from a practical point of view, it is crucial to have efficient and fast heuristic algorithms that avoid thorough search. In this investigation we design and implement heuristic algorithms, based on the frequency domain, which are applied on the coefficients of the discrete transform of the cosine and wavelets. Also, we propose temporal domain algorithms such as block-matching algorithms, which focus your search on the maximum coincidence of the current image with the reference one. The algorithms used during the implementation of this research work were written with the mathematical programming language MATLAB. In addition, we review the basic concepts of image processing, video, compression algorithms and motion estimation frequently used. The evaluation of the algorithms was carried out with a set of images provided by a previous acquisition system. We show the improvement of visual quality, the amount of compressed or reconstructed information and the behavior of the methods in the search for similarities between pixels or images. Finally, we contribute to the dissemination of new lines of scientific research that lead to the expansion and improvement of the study, the generation of new knowledge, since it is a young area within the Education discipline of Nicaragua.

Optimization design of biorthogonal filter banks for image compression

The wavelet transform is more and more widely used in image and video compression. One of the best known algorithms in image compression is the SPIHT algorithm which involves the wavelet transform. As today the parallelization of the wavelet transform is sufficiently investigated this work deals with the parallelization of the compression algorithm itself as a next step.

Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image.
 The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.