Abstract: JPEG is one of the most extensively used image formats. Understanding the inherent characteristics of JPEG may play a useful role in digital image forensics. In this paper, we introduce JPEG error analysis to the study of image forensics. The main errors of JPEG include quantization, rounding, and truncation errors. Through theoretically analyzing the effects of these errors on single and double JPEG compression, we have developed three novel schemes for image forensics including identifying whether a bitmap image has previously been JPEG compressed, estimating the quantization steps of a JPEG image, and detecting the quantization table of a JPEG image. Extensive experimental results show that our new methods significantly outperform existing techniques especially for the images of small sizes. We also show that the new method can reliably detect JPEG image blocks which are as small as 8 × 8 pixels and compressed with quality factors as high as 98. This performance is important for analyzing and locating small tampered regions within a composite image.

Abstract: In Shannon theory, lossless source coding deals with the optimal compression of discrete sources Compressed sensing is a lossless coding strategy for analog sources by means of multiplication by real-valued matrices In this paper we study almost lossless analog compression for analog memoryless sources in an information-theoretic framework, in which the compressor or decompressor is constrained by various regularity conditions, in particular linearity of the compressor and Lipschitz continuity of the decompressor The fundamental limit is shown to the information dimension proposed by Renyi in 1959

Abstract: Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different quantization matrixes. However, when the primary compression and the secondary compression have the same quantization matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same quantization matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a “proper” randomly perturbed ratio. For different images, this universal “proper” ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.

Abstract: Microprocessor designers have been torn between tight constraints on the amount of on-chip cache memory and the high latency of off-chip memory, such as dynamic random access memory. Accessing off-chip memory generally takes an order of magnitude more time than accessing on-chip cache, and two orders of magnitude more time than executing an instruction. Computer systems and microarchitecture researchers have proposed using hardware data compression units within the memory hierarchies of microprocessors in order to improve performance, energy efficiency, and functionality. However, most past work, and all work on cache compression, has made unsubstantiated assumptions about the performance, power consumption, and area overheads of the proposed compression algorithms and hardware. It is not possible to determine whether compression at levels of the memory hierarchy closest to the processor is beneficial without understanding its costs. Furthermore, as we show in this paper, raw compression ratio is not always the most important metric. In this work, we present a lossless compression algorithm that has been designed for fast on-line data compression, and cache compression in particular. The algorithm has a number of novel features tailored for this application, including combining pairs of compressed lines into one cache line and allowing parallel compression of multiple words while using a single dictionary and without degradation in compression ratio. We reduced the proposed algorithm to a register transfer level hardware design, permitting performance, power consumption, and area estimation. Experiments comparing our work to previous work are described.

Abstract: Data compression is also called as source coding. It is the process of encoding information using fewer bits than an uncoded representation is also making a use of specific encoding schemes. Compression is a technology for reducing the quantity of data used to represent any content without excessively reducing the quality of the picture. It also reduces the number of bits required to store and/or transmit digital media. Compression is a technique that makes storing easier for large amount of data. There are various techniques available for compression in my paper work , I have analyzed Huffman algorithm and compare it with other common compression techniques like Arithmetic, LZW and Run Length Encoding.

Abstract: Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.

Abstract: Conventional image/video sensors acquire visual information from a scene in time-quantized fashion at some predetermined frame rate. Each frame carries the information from all pixels, regardless of whether or not this information has changed since the last frame had been acquired, which is usually not long ago. This method obviously leads, depending on the dynamic contents of the scene, to a more or less high degree of redundancy in the image data. Acquisition and handling of these dispensable data consume valuable resources; sophisticated and resource-hungry video compression methods have been developed to deal with these data.

Abstract: The need for an efficient technique for compression of Images ever increasing because the raw images need large amounts of disk space seems to be a big disadvantage during transmission & storage. Even though there are so many compression technique already present a better technique which is faster, memory efficient and simple surely suits the requirements of the user. In this paper we proposed the Lossless method of image compression and decompression using a simple coding technique called Huffman coding. This technique is simple in implementation and utilizes less memory. A software algorithm has been developed and implemented to compress and decompress the given image using Huffman coding techniques in a MATLAB platform.

Abstract: Sharpness is one of the most determining factors in the perceptual assessment of image quality. Objective image sharpness measures may play important roles in the design and optimization of visual perception-based auto-focus systems and image enhancement, restoration and compression algorithms. Here we propose a new sharpness measure where sharpness is identified as strong local phase coherence evaluated in the complex wavelet transform domain. Our test using the LIVE blur database shows that the proposed algorithm correlates well with subjective quality evaluations. An additional advantage of our approach is that other image distortions such as compression, median filtering and noise contamination that may affect perceptual sharpness can also be detected.

Abstract: We show that optimal protocols for noisy channel coding of public or private information over either classical or quantum channels can be directly constructed from two more primitive information-theoretic tools: privacy amplification and information reconciliation, also known as data compression with side information. We do this in the one-shot scenario of structureless resources, and formulate our results in terms of the smooth min- and max-entropy. In the context of classical information theory, this shows that essentially all two-terminal protocols can be reduced to these two primitives, which are in turn governed by the smooth min- and max-entropies, respectively. In the context of quantum information theory, the recently-established duality of these two protocols means essentially all two-terminal protocols can be constructed using just a single primitive.

Abstract: We introduce an alternative Lempel-Ziv text parsing, LZ-End, that converges to the entropy and in practice gets very close to LZ77. LZ-End forces sources to finish at the end of a previous phrase. Most Lempel-Ziv parsings can decompress the text only from the beginning. LZ-End is the only parsing we know of able of decompressing arbitrary phrases in optimal time, while staying closely competitive with LZ77, especially on highly repetitive collections, where LZ77 excells. Thus LZ-End is ideal as a compression format for highly repetitive sequence databases, where access to individual sequences is required, and it also opens the door to compressed indexing schemes for such collections.

Abstract: A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.

Abstract: Systems for hardware-accelerated lossless data compression are described. At least some embodiments include data compression apparatus that includes a plurality of hash memories each associated with a different lane of a plurality of lanes (each lane including data bytes of a data unit being received by the compression apparatus), an array including array elements each including a plurality of validity bits (each validity bit within an array element corresponding to a different lane of the plurality of lanes), control logic that initiates a read of a hash memory entry if a corresponding validity bit indicates that said entry is valid, and an encoder that compresses at least the data bytes for the lane associated with the hash memory comprising the valid entry if said valid entry comprises data that matches the lane data bytes.

Abstract: Increasing the image size of a video sequence aggravates the memory bandwidth problem of a video coding system. Despite many embedded compression (EC) algorithms proposed to overcome this problem, no lossless EC algorithm able to handle high-definition (HD) size video sequences has been proposed thus far. In this paper, a lossless EC algorithm for HD video sequences and related hardware architecture is proposed. The proposed algorithm consists of two steps. The first is a hierarchical prediction method based on pixel averaging and copying. The second step involves significant bit truncation (SBT) which encodes prediction errors in a group with the same number of bits so that the multiple prediction errors are decoded in a clock cycle. The theoretical lower bound of the compression ratio of the SBT coding was also derived. Experimental results have shown a 60% reduction of memory bandwidth on average. Hardware implementation results have shown that a throughput of 14.2 pixels/cycle can be achieved with 36 K gates, which is sufficient to handle HD-size video sequences in real time.

Abstract: Motivated by applications to sensor networks and privacy preserving databases, we consider the problem of functional compression. The objective is to separately compress possibly correlated discrete sources such that an arbitrary but fixed deterministic function of those sources can be computed given the compressed data from each source. We consider both the lossless and lossy computation of a function. Specifically, we present results of the rate regions for three instances of the problem where there are two sources: 1) lossless computation where one source is available at the decoder; 2) under a special condition, lossless computation where both sources are separately encoded; and 3) lossy computation where one source is available at the decoder. For all of these instances, we present a layered architecture for distributed coding: first preprocess data at each source using colorings of certain characteristic graphs and then use standard distributed source coding (a la Slepian and Wolfs scheme) to compress them. For the first instance, our results extend the approach developed by Orlitsky and Roche (2001) in the sense that our scheme requires simpler structure of coloring rather than independent sets as in the previous case. As an intermediate step to obtain these results, we obtain an asymptotic characterization of conditional graph coloring for an OR product of graphs generalizing a result of Korner (1973), which should be of interest in its own right.

Abstract: We propose an algorithm to embed data directly in the bitstream of JPEG imagery. The motivation for this approach is that images are seldom available in uncompressed form. Algorithms that operate in spatial domain, or even in coefficient domain, require full (or at best) partial decompression. Our approach exploits the fact that only a fraction of JPEG code space is actually used by available encoders. Data embedding is performed by mapping a used variable length code (VLC) to an unused VLC. However, standard viewers unaware of the change will not properly display the image. We address this problem by a novel error concealment technique. Concealment works by remapping run/size values of marked VLCs so that standard viewers do not lose synchronization and displays the image with minimum loss of quality. It is possible for the embedded image to be visually identical to the original even though the two files are bitwise different. The algorithm is fast and transparent and embedding is reversible and file-size preserving. Under certain circumstances, file size may actually decrease despite carrying a payload.

Abstract: Recently, a number of digital image forensic techniques have been developed which are capable of identifying an image's origin, tracing its processing history, and detecting image forgeries. Though these techniques are capable of identifying standard image manipulations, they do not address the possibility that anti-forensic operations may be designed and used to hide evidence of image tampering. In this paper, we propose an anti-forensic operation capable of removing blocking artifacts from a previously JPEG compressed image. Furthermore, we show that by using this operation along with another anti-forensic operation which we recently proposed, we are able to fool forensic methods designed to detect evidence of JPEG compression in decoded images, determine an image's origin, detect double JPEG compression, and identify cut-and-paste image forgeries.

Abstract: With increasing quality requirements for multimedia communications, audio codecs must maintain both high quality and low delay. Typically, audio codecs offer either low delay or high quality, but rarely both. We propose a codec that simultaneously addresses both these requirements, with a delay of only 8.7 ms at 44.1 kHz. It uses gain-shape algebraic vector quantization in the frequency domain with time-domain pitch prediction. We demonstrate that the proposed codec operating at 48 kb/s and 64 kb/s out-performs both G.722.1C and MP3 and has quality comparable to AAC-LD, despite having less than one fourth of the algorithmic delay of these codecs.

Abstract: Preface. 1. Principles of digital image compression. 2. Compression algorithm fundamentals. 3. CCITT facsimile compression standards. 4. JBIG compression standard. 5. JPEG compression standard. 6. Digital video compression standards. 7. Digital image compression: advanced topics. Appendix A: Mathematical descriptions. Appendix B: Fast DCT algorithms. Glossary. Information on ISO/IEC standards. Information on ITU standards. Bibliography. Index.

Abstract: A robust image watermarking scheme in the ridgelet transform domain is proposed in this paper. Due to the use of the ridgelet domain, sparse representation of an image which deals with line singularities is obtained. In order to achieve more robustness and transparency, the watermark data is embedded in selected blocks of the host image by modifying the amplitude of the ridgelet coefficients which represent the most energetic direction. Since the probability distribution function of the ridgelet coefficients is not known, we propose a universally optimum decoder to perform the watermark extraction in a distribution-independent fashion. Decoder extracts the watermark data using the variance of the ridgelet coefficients of the most energetic direction in each block. Furthermore, since the decoder needs the noise variance to perform decoding, a robust noise estimation scheme is proposed. Moreover, the implementation of error correction codes on the proposed method is investigated. Analytical derivation of bit error probability is also carried out and experimental results prove its accuracy. Simulation also shows outstanding robustness of the proposed scheme against common attacks, especially additive white noise and JPEG compression.

Abstract: Encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application in the context of speech coding based on sparse linear prediction. In particular, a compressed sensing method can be devised to compute a sparse approximation of speech in the residual domain when sparse linear prediction is involved. We compare the method of computing a sparse prediction residual with the optimal technique based on an exhaustive search of the possible nonzero locations and the well known Multi-Pulse Excitation, the first encoding technique to introduce the sparsity concept in speech coding. Experimental results demonstrate the potential of compressed sensing in speech coding techniques, offering high perceptual quality with a very sparse approximated prediction residual.

Abstract: Techniques for storing and manipulating tabular data are provided. According to one embodiment, a user may control whether tabular data is stored in row-level or column-major format. Furthermore, the user may control the level of data compression to achieve an optimal balance between query performance and compression ratios. Tabular data from within the same table may be stored in both column-major and row-major format and compressed at different levels. In addition, tabular data can migrate between column-major format and row-major format in response to various events. For example, in response to a request to update or lock a row stored in column-major format, the row may be migrated and subsequently stored into row-major format. In one embodiment, table partitions are used to enhance data compression techniques. For example, compression tests are performed on a representative table partition, and a compression map is generated and applied to other table partitions.

Abstract: More than two decades of research in digital video technologies, together with the emergence of successful international standards for digital video compression, have led to a wide variety of digital video products using video compression for professional and consumer applications. Although many of these video compression standards share common and/or similar coding tools, there is currently no explicit way to exploit such commonalities at the level of the specifications nor at the level of implementations. Moreover, the possibility of taking advantage of the benefits of the continuous improvements of coding technology is only possible by replacing an old standard with a new one. This usually results in the replacement of the existing multimedia devices with new ones capable of handling the new deployed standards. Such necessity is not always well accept-ed by the public and professionals for obvious reasons.