Showing papers on "Data compression published in 2011"

A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS

Abstract: The biomimetic CMOS dynamic vision and image sensor described in this paper is based on a QVGA (304×240) array of fully autonomous pixels containing event-based change detection and pulse-width-modulation (PWM) imaging circuitry. Exposure measurements are initiated and carried out locally by the individual pixel that has detected a change of brightness in its field-of-view. Pixels do not rely on external timing signals and independently and asynchronously request access to an (asynchronous arbitrated) output channel when they have new grayscale values to communicate. Pixels that are not stimulated visually do not produce output. The visual information acquired from the scene, temporal contrast and grayscale data, are communicated in the form of asynchronous address-events (AER), with the grayscale values being encoded in inter-event intervals. The pixel-autonomous and massively parallel operation ideally results in lossless video compression through complete temporal redundancy suppression at the pixel level. Compression factors depend on scene activity and peak at ~1000 for static scenes. Due to the time-based encoding of the illumination information, very high dynamic range - intra-scene DR of 143 dB static and 125 dB at 30 fps equivalent temporal resolution - is achieved. A novel time-domain correlated double sampling (TCDS) method yields array FPN of 56 dB (9.3 bit) for >10 Lx illuminance.

Performance Analysis of $n$ -Channel Symmetric FEC-Based Multiple Description Coding for OFDM Networks

Abstract: Recently, multiple description source coding has emerged as an attractive framework for robust multimedia transmission over packet erasure channels. In this paper, we mathematically analyze the performance of n-channel symmetric FEC-based multiple description coding for a progressive mode of transmission over orthogonal frequency division multiplexing (OFDM) networks in a frequency-selective slowly-varying Rayleigh faded environment. We derive the expressions for the bounds of the throughput and distortion performance of the system in an explicit closed form, whereas the exact performance is given by an expression in the form of a single integration. Based on this analysis, the performance of the system can be numerically evaluated. Our results show that at high SNR, the multiple description encoder does not need to fine-tune the optimization parameters of the system due to the correlated nature of the subcarriers. It is also shown that, despite the bursty nature of the errors in a slow fading environment, FEC-based multiple description coding without temporal coding provides a greater advantage for smaller description sizes.

Efficient storage of high throughput DNA sequencing data using reference-based compression

Abstract: Data storage costs have become an appreciable proportion of total cost in the creation and analysis of DNA sequence data. Of particular concern is that the rate of increase in DNA sequencing is significantly outstripping the rate of increase in disk storage capacity. In this paper we present a new reference-based compression method that efficiently compresses DNA sequences for storage. Our approach works for resequencing experiments that target well-studied genomes. We align new sequences to a reference genome and then encode the differences between the new sequence and the reference genome for storage. Our compression method is most efficient when we allow controlled loss of data in the saving of quality information and unaligned sequences. With this new compression method we observe exponential efficiency gains as read lengths increase, and the magnitude of this efficiency gain can be controlled by changing the amount of quality information stored. Our compression method is tunable: The storage of quality scores and unaligned sequences may be adjusted for different experiments to conserve information or to minimize storage costs, and provides one opportunity to address the threat that increasing DNA sequence volumes will overcome our ability to store the sequences.

Local Intensity Order Pattern for feature description

Abstract: This paper presents a novel method for feature description based on intensity order. Specifically, a Local Intensity Order Pattern(LIOP) is proposed to encode the local ordinal information of each pixel and the overall ordinal information is used to divide the local patch into subregions which are used for accumulating the LIOPs respectively. Therefore, both local and overall intensity ordinal information of the local patch are captured by the proposed LIOP descriptor so as to make it a highly discriminative descriptor. It is shown that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation but also robust to many other geometric and photometric transformations such as viewpoint change, image blur and JEPG compression. The proposed descriptor has been evaluated on the standard Oxford dataset and four additional image pairs with complex illumination changes. The experimental results show that the proposed descriptor obtains a significant improvement over the existing state-of-the-art descriptors.

High-dimensional signature compression for large-scale image classification

Abstract: We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 105 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases — ImageNet and a dataset of lM Flickr images — showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.

Technical overview of VP8, an open source video codec for the web

Abstract: VP8 is an open source video compression format supported by a consortium of technology companies. This paper provides a technical overview of the format, with an emphasis on its unique features. The paper also discusses how these features benefit VP8 in achieving high compression efficiency and low decoding complexity at the same time.

Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data

Abstract: Since the human visual system (HVS) is the ultimate assessor of image quality, current research on the design of objective image quality metrics tends to include an important feature of the HVS, namely, visual attention. Different metrics for image quality prediction have been extended with a computational model of visual attention, but the resulting gain in reliability of the metrics so far was variable. To better understand the basic added value of including visual attention in the design of objective metrics, we used measured data of visual attention. To this end, we performed two eye-tracking experiments: one with a free-looking task and one with a quality assessment task. In the first experiment, 20 observers looked freely to 29 unimpaired original images, yielding us so-called natural scene saliency (NSS). In the second experiment, 20 different observers assessed the quality of distorted versions of the original images. The resulting saliency maps showed some differences with the NSS, and therefore, we applied both types of saliency to four different objective metrics predicting the quality of JPEG compressed images. For both types of saliency the performance gain of the metrics improved, but to a larger extent when adding the NSS. As a consequence, we further integrated NSS in several state-of-the-art quality metrics, including three full-reference metrics and two no-reference metrics, and evaluated their prediction performance for a larger set of distortions. By doing so, we evaluated whether and to what extent the addition of NSS is beneficial to objective quality prediction in general terms. In addition, we address some practical issues in the design of an attention-based metric. The eye-tracking data are made available to the research community .

Interpolation of Video Compression Frames

Abstract: Coding techniques for a video image compression system involve improving an image quality of a sequence of two or more bi-directionally predicted intermediate frames, where each of the frames includes multiple pixels. One method involves determining a brightness value of at least one pixel of each bi-directionally predicted intermediate frame in the sequence as an equal average of brightness values of pixels in non-bidirectionally predicted frames bracketing the sequence of bi-directionally predicted intermediate frames. The brightness values of the pixels in at least one of the non-bidirectionally predicted frames is converted from a non-linear representation.

Anti-forensics of digital image compression

Abstract: As society has become increasingly reliant upon digital images to communicate visual information, a number of forensic techniques have been developed to verify the authenticity of digital images. Amongst the most successful of these are techniques that make use of an image's compression history and its associated compression fingerprints. Little consideration has been given, however, to anti-forensic techniques capable of fooling forensic algorithms. In this paper, we present a set of anti-forensic techniques designed to remove forensically significant indicators of compression from an image. We do this by first developing a generalized framework for the design of anti-forensic techniques to remove compression fingerprints from an image's transform coefficients. This framework operates by estimating the distribution of an image's transform coefficients before compression, then adding anti-forensic dither to the transform coefficients of a compressed image so that their distribution matches the estimated one. We then use this framework to develop anti-forensic techniques specifically targeted at erasing compression fingerprints left by both JPEG and wavelet-based coders. Additionally, we propose a technique to remove statistical traces of the blocking artifacts left by image compression algorithms that divide an image into segments during processing. Through a series of experiments, we demonstrate that our anti-forensic techniques are capable of removing forensically detectable traces of image compression without significantly impacting an image's visual quality. Furthermore, we show how these techniques can be used to render several forms of image tampering such as double JPEG compression, cut-and-paste image forgery, and image origin falsification undetectable through compression-history-based forensic means.

Optimizing a Tone Curve for Backward-Compatible High Dynamic Range Image and Video Compression

Abstract: For backward compatible high dynamic range (HDR) video compression, the HDR sequence is reconstructed by inverse tone-mapping a compressed low dynamic range (LDR) version of the original HDR content. In this paper, we show that the appropriate choice of a tone-mapping operator (TMO) can significantly improve the reconstructed HDR quality. We develop a statistical model that approximates the distortion resulting from the combined processes of tone-mapping and compression. Using this model, we formulate a numerical optimization problem to find the tone-curve that minimizes the expected mean square error (MSE) in the reconstructed HDR sequence. We also develop a simplified model that reduces the computational complexity of the optimization problem to a closed-form solution. Performance evaluations show that the proposed methods provide superior performance in terms of HDR MSE and SSIM compared to existing tone-mapping schemes. It is also shown that the LDR image quality resulting from the proposed methods matches that produced by perceptually-based TMOs.

Compressing the incompressible with ISABELA: in-situ reduction of spatio-temporal data

Abstract: Modern large-scale scientific simulations running on HPC systems generate data in the order of terabytes during a single run. To lessen the I/O load during a simulation run, scientists are forced to capture data infrequently, thereby making data collection an inherently lossy process. Yet, lossless compression techniques are hardly suitable for scientific data due to its inherently random nature; for the applications used here, they offer less than 10% compression rate. They also impose significant overhead during decompression, making them unsuitable for data analysis and visualization that require repeated data access. To address this problem, we propose an effective method for In-situ Sort-And-B-spline Error-bounded Lossy Abatement (ISABELA) of scientific data that is widely regarded as effectively incompressible. With ISABELA, we apply a preconditioner to seemingly random and noisy data along spatial resolution to achieve an accurate fitting model that guarantees a ≥ 0.99 correlation with the original data. We further take advantage of temporal patterns in scientific data to compress data by ≈ 85%, while introducing only a negligible overhead on simulations in terms of runtime. ISABELA significantly outperforms existing lossy compression methods, such as Wavelet compression. Moreover, besides being a communication-free and scalable compression technique, ISABELA is an inherently local decompression method, namely it does not decode the entire data, making it attractive for random access.

A Real-Time ECG Data Compression and Transmission Algorithm for an e-Health Device

Abstract: This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

Lossy Compression and Iterative Reconstruction for Encrypted Image

Abstract: This work proposes a novel scheme for lossy compression of an encrypted image with flexible compression ratio. A pseudorandom permutation is used to encrypt an original image, and the encrypted data are efficiently compressed by discarding the excessively rough and fine information of coefficients generated from orthogonal transform. After receiving the compressed data, with the aid of spatial correlation in natural image, a receiver can reconstruct the principal content of the original image by iteratively updating the values of coefficients. This way, the higher the compression ratio and the smoother the original image, the better the quality of the reconstructed image.

Lossless Data Embedding Using Generalized Statistical Quantity Histogram

Abstract: Histogram-based lossless data embedding (LDE) has been recognized as an effective and efficient way for copyright protection of multimedia. Recently, a LDE method using the statistical quantity histogram has achieved good performance, which utilizes the similarity of the arithmetic average of difference histogram (AADH) to reduce the diversity of images and ensure the stable performance of LDE. However, this method is strongly dependent on some assumptions, which limits its applications in practice. In addition, the capacities of the images with the flat AADH, e.g., texture images, are a little bit low. For this purpose, we develop a novel framework for LDE by incorporating the merits from the generalized statistical quantity histogram (GSQH) and the histogram-based embedding. Algorithmically, we design the GSQH driven LDE framework carefully so that it: (1) utilizes the similarity and sparsity of GSQH to construct an efficient embedding carrier, leading to a general and stable framework; (2) is widely adaptable for different kinds of images, due to the usage of the divide-and-conquer strategy; (3) is scalable for different capacity requirements and avoids the capacity problems caused by the flat histogram distribution; (4) is conditionally robust against JPEG compression under a suitable scale factor; and (5) is secure for copyright protection because of the safe storage and transmission of side information. Thorough experiments over three kinds of images demonstrate the effectiveness of the proposed framework.

A DCT Approximation for Image Compression

Abstract: An orthogonal approximation for the 8-point discrete cosine transform (DCT) is introduced. The proposed transformation matrix contains only zeros and ones; multiplications and bit-shift operations are absent. Close spectral behavior relative to the DCT was adopted as design criterion. The proposed algorithm is superior to the signed discrete cosine transform. It could also outperform state-of-the-art algorithms in low and high image compression scenarios, exhibiting at the same time a comparable computational complexity.

A Multiscale Metric for 3D Mesh Visual Quality Assessment

Abstract: Many processing operations are nowadays applied on 3D meshes like compression, watermarking, remeshing and so forth; these processes are mostly driven and/or evaluated using simple distortion measures like the Hausdorff distance and the root mean square error, however these measures do not correlate with the human visual perception while the visual quality of the processed meshes is a crucial issue. In that context we introduce a full-reference 3D mesh quality metric; this metric can compare two meshes with arbitrary connectivity or sampling density and produces a score that predicts the distortion visibility between them; a visual distortion map is also created. Our metric outperforms its counterparts from the state of the art, in term of correlation with mean opinion scores coming from subjective experiments on three existing databases. Additionally, we present an application of this new metric to the improvement of rate-distortion evaluation of recent progressive compression algorithms.

Compressive sampling for accelerometer signals in structural health monitoring

Abstract: In structural health monitoring (SHM) of civil structures, data compression is often needed to reduce the cost of data transfer and storage, because of the large volumes of sensor data generated from the monitoring system. The traditional framework for data compression is to first sample the full signal and, then to compress it. Recently, a new data compression method named compressive sampling (CS) that can acquire the data directly in compressed form by using special sensors has been presented. In this article, the potential of CS for data compression of vibration data is investigated using simulation of the CS sensor algorithm. For reconstruction of the signal, both wavelet and Fourier orthogonal bases are examined. The acceleration data collected from the SHM system of Shandong Binzhou Yellow River Highway Bridge is used to analyze the data compression ability of CS. For comparison, both the wavelet-based and Huffman coding methods are employed to compress the data. The results show that the values of compression ratios achieved using CS are not high, because the vibration data used in SHM of civil structures are not naturally sparse in the chosen bases.

Detecting Recompression of JPEG Images via Periodicity Analysis of Compression Artifacts for Tampering Detection

Abstract: Due to the popularity of JPEG as an image compression standard, the ability to detect tampering in JPEG images has become increasingly important. Tampering of compressed images often involves recompression and tends to erase traces of tampering found in uncompressed images. In this paper, we present a new technique to discover traces caused by recompression. We assume all source images are in JPEG format and propose to formulate the periodic characteristics of JPEG images both in spatial and transform domains. Using theoretical analysis, we design a robust detection approach which is able to detect either block-aligned or misaligned recompression. Experimental results demonstrate the validity and effectiveness of the proposed approach, and also show it outperforms existing methods.

An Adaptive Resolution Asynchronous ADC Architecture for Data Compression in Energy Constrained Sensing Applications

Abstract: An adaptive resolution (AR) asynchronous analog-to-digital converter (ADC) architecture is presented. Data compression is achieved by the inherent signal dependent sampling rate of the asynchronous architecture. An AR algorithm automatically varies the ADC quantizer resolution based on the rate of change of the input. This overcomes the trade-off between dynamic range and input bandwidth typically seen in asynchronous ADCs. A prototype ADC fabricated in a 0.18 μm CMOS technology, and utilizing the subthreshold region of operation, achieves an equivalent maximum sampling rate of 50 kS/s, an SNDR of 43.2 dB, and consumes 25 μW from a 0.7 V supply. The ADC is also shown to provide data compression for accelerometer applications as a proof of concept demonstration.

Image compression using learned dictionaries by RLS-DLA and compared with K-SVD

Abstract: The recently presented recursive least squares dictionary learning algorithm (RLS-DLA) is tested in a general image compression application. Dictionaries are learned in the pixel domain and in the 9/7 wavelet domain, and then tested in a straightforward compression scheme. Results are compared with state-of-the-art compression methods. The proposed compression scheme using RLS-DLA learned dictionaries in the 9/7 wavelet domain performs better than using dictionaries learned by other methods. The compression rate is just below the JPEG-2000 rate which is promising considering the simple entropy coding used.

SQUISH: an online approach for GPS trajectory compression

Abstract: GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data generated. The size of this data makes it difficult to transmit it over a mobile network and to analyze it to extract useful patterns. Numerous compression algorithms have been proposed to reduce the size of trajectory data sets; however these methods often lose important information essential to location-based applications such as object's position, time and speed. This paper describes the Spatial QUalIty Simplification Heuristic (SQUISH) method that demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression. Performance is evaluated by comparison with three competing trajectory compression algorithms: Uniform Sampling, Douglas-Peucker and Dead Reckoning.

Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error

Abstract: This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video which operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, we target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their associated macroblock prediction error, which is different from the approaches based on the motion vector attributes such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve robustness while maintaining a low prediction error level. The secret message bitstream is embedded in the least significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based on the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vector attribute-based method from the literature.

New Depth Coding Techniques With Utilization of Corresponding Video

Abstract: With the development of 3D display and interactive multimedia systems, new 3D video applications, such as 3DTV and Free Viewpoint Video, are attracting significant interests. In order to enable these new applications, new data formats including captured 2D video sequences and corresponding depth maps have been proposed. Compared to conventional video frames, depth maps have very different characteristics. First, they typically consist of homogeneous areas partitioned by sharp edges representing depth discontinuities, while the sharp discontinuities play very important roles in view rendering. Second, there exists structure similarity between depth map and corresponding video, in which the edges in depth exhibit quite similar behaviors as the edges in the corresponding video. In conventional video coding techniques with transforms followed by quantization, there usually exist large artifacts along sharp edges and it costs significant more bits to represent the edges with higher accuracy. In order to suppress the coding artifacts while preserving edges, and to better compress depth information, we propose in this paper two new depth compression techniques: Trilateral Filter and Sparse Dyadic Mode. Both techniques utilize the structure similarity between depth and corresponding video while focusing on different aspects in depth compression. As a new in-loop filter, Trilateral Filter is designed to filter depth with coding artifacts based on the proximity of pixel positions, the similarity among depth samples, as well as the similarity among the collocated pixels in the video frame. While Sparse Dyadic Mode is used as an intra mode to reconstruct depth map with sparse representations of depth blocks and effective reference of edge information from video frames. With these two new coding tools, we can achieve up to about 1.5 dB gain on rendering quality as compared to depth sequences coded using MVC under the same coding rate.

An Analysis of Visual Adaptation and Contrast Perception for Tone Mapping

Abstract: Tone Mapping is the problem of compressing the range of a High-Dynamic Range image so that it can be displayed in a Low-Dynamic Range screen, without losing or introducing novel details: The final image should produce in the observer a sensation as close as possible to the perception produced by the real-world scene. We propose a tone mapping operator with two stages. The first stage is a global method that implements visual adaptation, based on experiments on human perception, in particular we point out the importance of cone saturation. The second stage performs local contrast enhancement, based on a variational model inspired by color vision phenomenology. We evaluate this method with a metric validated by psychophysical experiments and, in terms of this metric, our method compares very well with the state of the art.

Interpretations of Directed Information in Portfolio Theory, Data Compression, and Hypothesis Testing

Abstract: We investigate the role of directed information in portfolio theory, data compression, and statistics with causality constraints. In particular, we show that directed information is an upper bound on the increment in growth rates of optimal portfolios in a stock market due to causal side information. This upper bound is tight for gambling in a horse race, which is an extreme case of stock markets. Directed information also characterizes the value of causal side information in instantaneous compression and quantifies the benefit of causal inference in joint compression of two stochastic processes. In hypothesis testing, directed information evaluates the best error exponent for testing whether a random process Y causally influences another process X or not. These results lead to a natural interpretation of directed information I(Yn → Xn) as the amount of information that a random sequence Yn = (Y1,Y2,..., Yn) causally provides about another random sequence Xn = (X1,X2,...,Xn). A new measure, directed lautum information, is also introduced and interpreted in portfolio theory, data compression, and hypothesis testing.

SSIM-Based Perceptual Rate Control for Video Coding

Abstract: The quality of video is ultimately judged by human eye; however, mean squared error and the like that have been used as quality metrics are poorly correlated with human perception. Although the characteristics of human visual system have been incorporated into perceptual-based rate control, most existing schemes do not take rate-distortion optimization into consideration. In this paper, we use the structural similarity index as the quality metric for rate-distortion modeling and develop an optimum bit allocation and rate control scheme for video coding. This scheme achieves up to 25% bit-rate reduction over the JM reference software of H.264. Under the rate-distortion optimization framework, the proposed scheme can be easily integrated with the perceptual-based mode decision scheme. The overall bit-rate reduction may reach as high as 32% over the JM reference software.

Unequal Error Protection Using Fountain Codes With Applications to Video Communication

Abstract: Application-layer forward error correction (FEC) is used in many multimedia communication systems to address the problem of packet loss in lossy packet networks. One powerful form of application-layer FEC is unequal error protection which protects the information symbols according to their importance. We propose a method for unequal error protection with a Fountain code. When the information symbols were partitioned into two protection classes (most important and least important), our method required a smaller transmission bit budget to achieve low bit error rates compared to the two state-of-the-art techniques. We also compared our method to the two state-of-the-art techniques for video unicast and multicast over a lossy network. Simulations for the scalable video coding (SVC) extension of the H.264/AVC standard showed that our method required a smaller transmission bit budget to achieve high-quality video.

Simultaneous fusion, compression, and encryption of multiple images

Abstract: We report a new spectral multiple image fusion analysis based on the discrete cosine transform (DCT) and a specific spectral filtering method. In order to decrease the size of the multiplexed file, we suggest a procedure of compression which is based on an adapted spectral quantization. Each frequency is encoded with an optimized number of bits according its importance and its position in the DC domain. This fusion and compression scheme constitutes a first level of encryption. A supplementary level of encryption is realized by making use of biometric information. We consider several implementations of this analysis by experimenting with sequences of gray scale images. To quantify the performance of our method we calculate the MSE (mean squared error) and the PSNR (peak signal to noise ratio). Our results consistently improve performances compared to the well-known JPEG image compression standard and provide a viable solution for simultaneous compression and encryption of multiple images.

Accelerating Wavelet Lifting on Graphics Hardware Using CUDA

Abstract: The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as massively parallel coprocessors through NVidia's CUDA compute paradigm The three main hardware architectures for the 2D DWT (row-column, line-based, block-based) are shown to be unsuitable for a CUDA implementation Our CUDA-specific design can be regarded as a hybrid method between the row-column and block-based methods We achieve considerable speedups compared to an optimized CPU implementation and earlier non-CUDA-based GPU DWT methods, both for 2D images and 3D volume data Additionally, memory usage can be reduced significantly compared to previous GPU DWT methods The method is scalable and the fastest GPU implementation among the methods considered A performance analysis shows that the results of our CUDA-specific design are in close agreement with our theoretical complexity analysis

Compression via Matroids: A Randomized Polynomial Kernel for Odd Cycle Transversal

Abstract: The Odd Cycle Transversal problem (OCT) asks whether a given graph can be made bipartite by deleting at most $k$ of its vertices. In a breakthrough result Reed, Smith, and Vetta (Operations Research Letters, 2004) gave a $\BigOh(4^kkmn)$ time algorithm for it, the first algorithm with polynomial runtime of uniform degree for every fixed $k$. It is known that this implies a polynomial-time compression algorithm that turns OCT instances into equivalent instances of size at most $\BigOh(4^k)$, a so-called kernelization. Since then the existence of a polynomial kernel for OCT, i.e., a kernelization with size bounded polynomially in $k$, has turned into one of the main open questions in the study of kernelization. This work provides the first (randomized) polynomial kernelization for OCT. We introduce a novel kernelization approach based on matroid theory, where we encode all relevant information about a problem instance into a matroid with a representation of size polynomial in $k$. For OCT, the matroid is built to allow us to simulate the computation of the iterative compression step of the algorithm of Reed, Smith, and Vetta, applied (for only one round) to an approximate odd cycle transversal which it is aiming to shrink to size $k$. The process is randomized with one-sided error exponentially small in $k$, where the result can contain false positives but no false negatives, and the size guarantee is cubic in the size of the approximate solution. Combined with an $\BigOh(\sqrt{\log n})$-approximation (Agarwal et al., STOC 2005), we get a reduction of the instance to size $\BigOh(k^{4.5})$, implying a randomized polynomial kernelization.