Performance Evaluation of Video Compression Using Different Wavelets and Tensors for Multimedia Applications

A lot of attentions has been paid by the researchers for lossy image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality, is still a research issue. In this paper, we proposed an image compression using adaptive scanning, in which DWT(discrete wavelet transform) is performed to source image, DCT(discrete cosine transform) is performed to obtained DWT elements. Next, non zero quantized DCT (discrete cosine transform) coefficients are represented in less number of bits. First, the source image is divided into \(16\times 16\) grids and then DWT transformed, later the obtained elements are divided into \(8\times 8\) grids and then DCT is performed, quantized, differential encoded and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore the indexed vector is divided into two indexed vectors namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.

PurposeCone‐beam computerized tomography (CBCT) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data.Materials and methodsAt first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients.ResultsAmong three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT.ConclusionsVideo compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.

This paper suggests a new image compression scheme, using the discrete wavelet transformation (DWT), which is based on attempting to preserve the texturally important image characteristics. The main point of the proposed methodology lies on that, the image is divided into regions of textural significance employing textural descriptors as criteria and fuzzy clustering methodologies. These textural descriptors include cooccurrence matrices based measures and coherence analysis derived features. While rival image compression methodologies utilizing the DWT apply it to the whole original image, the herein presented novel approach involves a more sophisticated scheme in the application of the DWT. More specifically, the DWT is applied separately to each region in which the original image is partitioned and, depending on how it has been texturally clustered, its relative number of the wavelet coefficients to keep is then determined. Therefore, different compression ratios are applied to the above specified image regions. The reconstruction process of the original image involves the linear combination of its corresponding reconstructed regions. An experimental study is conducted to qualitatively assessing the proposed compression approach. Moreover, this experimental study aims at comparing different textural measures in terms of their results concerning the quality of the reconstructed image.

The subject of study. A new algorithm for real-time data compression in a video surveillance system from an unmanned aerial vehicle is proposed. The algorithm makes it possible to obtain different degrees of video data compression using different codecs for specific video recording conditions (UAV altitude and flight speed, nature of the observed terrain, etc.), and also provides the ability to transmit video data to the flight control point via radio channel. A detailed analysis of the quality of the proposed algorithm for shooting in various video formats that meet international standards (HD and Full HD) was carried out, both in terms of visual perception and objective criteria. The purpose of the study was to conduct a comprehensive research on the problem of improving the quality of the video surveillance system from the UAV and to create a video compression algorithm that allows broadcasting video data to the control center in real time with high quality. Objectives. To develop and programmatically implement a video compression algorithm based on modern codecs for video surveillance sys­tems on board a UAV. To use modern video cameras and a microcomputer platform as hardware. To analyze the quality indicators of the algorithm in different conditions. Methods used. Evaluation of visual perception of video compression results, statistical analysis and processing of the parameters of the analyzed video sequence as a random spatio-temporal process. Results. A new video data compression algorithm for the UAV video surveillance system based on the Raspberry Pi 5 microcomputer using modern video compression codecs H.264 and H.265 was synthesized. The algorithm provides the required compression ratio and data transmission rate over the radio channel between the UAV and the flight control center. Detailed estimates of the quality of the algorithm are obtained. Conclusions. Scientific novelty of the results: A new universal video data compression algorithm for video surveillance systems on board UAVs with the ability to transmit video over a radio channel in real time has been proposed and implemented. The quality of the proposed algorithm is verified on real video surveillance results. The proposed algorithm is implemented in the Python software environment.

The digital world demands the transmission and storage of high quality video for streaming and broadcasting applications, the constraints are the network bandwidth and the memory of devices for the various multimedia and scientific applications, the video consists of spatial and temporal redundancies. The objective of any video compression algorithm is to eliminate the redundant information from the video signal during compression for effective transmission and storage. The correlation between the successive frames has not been exploited enough by the current compression algorithms. In this paper, a novel method for video compression is presented. The proposed model, applies the transformation on set of group of pictures (GOP). The high spatial correlation is achieved from the spatial and temporal redundancy of GOP by accordion representation and this helps to bypass the computationally demanding motion compensation step. The core idea of the proposed technique is to apply Tucker Decomposition (TD) on the Discrete Wavelet Transform (DWT) coefficients of the Accordion model of the GOP. We use DWT to separate the video in to different sub-images and TD to efficiently compact the energy of sub-images. The blocking artifacts will be considerably eliminated as the block size is huge. The proposed method attempts to reduce the spatial and temporal redundancies of the video signal to improve the compression ratio, computation time, and PSNR. The experimental results prove that the proposed method is efficient especially in high bit rate and with slow motion videos.

Presents two novel algorithms for compression of multilead electrocardiograms (MLECGs). In the first, the MLECG was decorrelated using a discrete cosine transform (DCT) and then a discrete wavelet transform (DWT) was applied on each channel after the removal of the least significant channels in the DCT domain. Significant compression ratios (CR) were achieved by retaining only the consequential DWT coefficients. The full potential of DWT was further exploited by appropriate quantization of these coefficients. Near perfect reconstruction was obtained for a CR of 1:8. This approach is highly suited for real time applications including that of high frequency Holter ECG. In the other algorithm, first level of compression was achieved by truncating the DWT while storing the highly significant coefficients from the truncated portion. Transform domain pole-zero modeling (TDPM) was then applied on the DWT compressed ECG to achieve higher compression ratios. In TDPM the DCT of a cycle of ECG is modeled as the impulse response of a pole-zero filter using Smith's (1983) method. Further compression was achieved by the application of vector quantization (VQ) on the model parameters. This algorithm resulted in a CR of 1:80 without significant distortion. The accuracy of reconstruction was checked using both qualitative and quantitative measures. The proposed algorithms were validated on both standard and local databases.

A watermark is embedded into a color image. The relationship between embedding and compression process is investigated. The compression ratio (CR) is adjusted from 2 to 15, and three gray-levels of the original image are changed, their average gray-levels are individually 126, 161 and 196. From the experimental results, there is an apparent inclination about higher gray-level makes higher PSNR. There are two kinds of compression methods in the compression process, one includes color space transform and sampling, and this method has higher CR, the other has not these steps and it has lower CR. In this paper the relationship of these parameters such as the gray-level of the image, the parameters of frequency domain are also discussed and analyzed. The relation between color saturation and normalized correlation (NC) ratio has been quantitatively analyzed, it is found that the extracted watermark has the best quality when the saturation factor is set to the value of 0.2, and the NC value is lower when the processing has lower color factor. The curve of NC drops gradually when the color factor is over 0.4. The main reason of this result is also discussed in the final discussion. In our proposed approach, a systematic analysis is performed. The adaptive methods are selected to obtain the optimum conditions to embed the watermark into an image.

Lossless compression schemes with progressive transmission capability play a key role in critical data storage, retrieval and transmission, when a large volume of data must be handled fast, without distortions, over large networks with limited bandwidth. Wavelet transforms present one such approach for the purpose of compression. Wavelet based compression methods, when combined with EZW (Embedded Zerotree Wavelet) and SPIHT (Set Partitioning in Hierarchical Trees) algorithms gives high compression ratio along with appreciable image quality which is very much necessary for huge database storage in Medical Centres and medical data transfer for the purpose of diagnosis. The Progressive quality of the compression methods facilitates the user to the best compressed image which does not have recognizable quality loss.In this study wide variety of CT and MRI (512x512) images are used to validate the usefulness of progressive property especially for telemedicine purpose where either single image or sequence of images are transmitted over computer networks at large distances so that they could be used in a multitude of applications.Our main goal is to compare different types of wavelets (Haar, db, Bior 4.4) for medical image compression because of the fact that there is no single wavelet, which will always provide the best performance.

This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).

Due to the high sampling rate, the recorded Electrocardiograms (ECG) data are huge. For storing and transmitting ECG data, wide spaces and more bandwidth are therefore needed. The ECG data are also very important to preprocessing and compress so that it is distributed and processed with less bandwidth and less space effectively. This manuscript is aimed at creating an effective ECG compression method. The reported ECG data are processed first in the pre-processing unit (ProUnit) in this method. In this unit, ECG data have been standardized and segmented. The resulting ECG data would then be sent to the Compression Unit (CompUnit). The unit consists of an algorithm for lossy compression (LosyComp), with a lossless algorithm for compression (LossComp). The randomness ECG data is transformed into high randomness data by the failure compression algorithm. The data's high redundancy is then used with the LosyComp algorithm to reach a high compression ratio (CR) with no degradation. The LossComp algorithms recommended in this manuscript are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). LossComp algorithms such as Arithmetic Encoding (Arithm) and Run Length Encoding (RLE) are also suggested. To evaluate the proposed method, we measure the Compression Time (CompTime), and Reconstruction Time (RecTime) (T), RMSE and CR. Simulation results suggest the highest output in compression ratio and in complexity by adding RLE after the DCT algorithm. The simulation findings indicate that the inclusion of RLE following the DCT algorithm increases performance in terms of CR and complexity. With CR = 55% with RMSE = 0:14 and above 94% with RMSE = 0:2, DCT as a LossComp algorithm was utilized initially, followed by RLE as a LossComp algorithm.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

JPEG2000 (j2k) is a highly popular format for image and video compression. It plays a major role in the rapidly growing applications of cloud based image classification. Considering limited network bandwidth, we propose an end-to-end deep learning framework to achieve faster and more accurate classification by directly training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients from j2k-compressed images without image reconstruction. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy. Furthermore, we present DWT-centric augmentation transformations to achieve more accurate classification without added cost. Achieving faster and more accurate classification for j2k encoded images, the proposed solution is well suited for joint compression and cloud-based image and video classification over limited channel bandwidth.

To assess retrospectively the effect of the Joint Photographic Experts Group 2000 (JPEG2000) compression ratio on the quality of thin-section computed tomographic (CT) images. In this institutional review board-approved investigation (protocol 238/2004), thin-section CT images were subjected to irreversible JPEG2000 compression by using five compression ratios (3:1, 5:1, 7:1, 9:1, and 11:1). Three radiologists independently evaluated 60 thin-section CT images, of various diseases, that were obtained with single-detector (weighted dose index, 14.4 mGy) and multidetector (weighted dose index, 9.8 mGy) CT. Toggling between the original and compressed images, readers had to identify the original image by using a forced-choice two-alternative model and to subjectively rank the quality of what they believed to be the compressed image. To assess the reader's ability to distinguish the compressed from the original image, a binomial test was used. Bonferroni correction was applied for all multiple tests. Images compressed with a ratio of 3:1 were not distinguishable from original images (P > .2 for all readers). With use of the 5:1 ratio, minor differences in appearance between the compressed and original images were seen by one of the three readers. With use of higher compression ratios (>/=7:1), all readers (P < .001) recognized the original image. The quality of more than 90% of the images compressed with a 7:1 or higher ratio was substantially degraded. Single-detector and multidetector CT results were not significantly different. The highest ratio that yielded visually lossless compression of thin-section CT images was 3:1. With the 5:1 ratio, there was minor image quality loss, while use of higher compression ratios (>/=7:1) caused substantial degradation of image quality and potential loss of diagnostic information.

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.

The main aim of image compression is to represent an image with minimum number of bits, so that the storage requirement can be reduced, thereby increasing the transmission rate without losing significant features of the image. The compression ratio is affected by noise, as it degrades the correlation between pixels. During capture, processing or transmission of the image, noise can occur. The noise possibly can be independent of or dependent on image content. On lossy image compression algorithms, the effect of noise has been studied in this paper. In order to study the effect of noise, the original images act as a reference to the reconstructed images. The reconstructed images are compared with the original images in terms of PSNR. The proposed image encoder integrates the features of curvelet transform with both radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) separately and results are presented for both the cases. The case studies which consider images with noise prove the superiority of the techniques in terms of highly acceptable PSNR values. The merits of the proposed technique are further exemplified by comparing the results with those of JPEG and JPEG 2000.