70 research outputs found

    Sample-Parallel Execution of EBCOT in Fast Mode

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    JPEG 2000’s most computationally expensive building block is the Embedded Block Coder with Optimized Truncation (EBCOT). This paper evaluates how encoders targeting a parallel architecture such as a GPU can increase their throughput in use cases where very high data rates are used. The compression efficiency in the less significant bit-planes is then often poor and it is beneficial to enable the Selective Arithmetic Coding Bypass style (fast mode) in order to trade a small loss in compression efficiency for a reduction of the computational complexity. More importantly, this style exposes a more finely grained parallelism that can be exploited to execute the raw coding passes, including bit-stuffing, in a sample-parallel fashion. For a latency- or memory critical application that encodes one frame at a time, EBCOT’s tier-1 is sped up between 1.1x and 2.4x compared to an optimized GPU-based implementation. When a low GPU occupancy has already been addressed by encoding multiple frames in parallel, the throughput can still be improved by 5% for high-entropy images and 27% for low-entropy images. Best results are obtained when enabling the fast mode after the fourth significant bit-plane. For most of the test images the compression rate is within 1% of the original

    Coding for the Optical Channel: the Ghost-Pulse Constraint

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    We consider a number of constrained coding techniques that can be used to mitigate a nonlinear effect in the optical fiber channel that causes the formation of spurious pulses, called ``ghost pulses.'' Specifically, if b1b2...bnb_1 b_2 ... b_{n} is a sequence of bits sent across an optical channel, such that bk=bl=bm=1b_k=b_l=b_m=1 for some k,l,mk,l,m (not necessarily all distinct) but bk+lm=0b_{k+l-m} = 0, then the ghost-pulse effect causes bk+lmb_{k+l-m} to change to 1, thereby creating an error. We design and analyze several coding schemes using binary and ternary sequences constrained so as to avoid patterns that give rise to ghost pulses. We also discuss the design of encoders and decoders for these coding schemes.Comment: 13 pages, 6 figures; accepted for publication in IEEE Transactions on Information Theor

    Editing MPEG2 video in compressed domain

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    As MPEG2 becomes the heart of most digital video applications, the need to edit an MPEG2 bitstream video in compressed domain has increased. To realize this need, two major technical issues must be resolved: decoder buffer and segment extraction. In the decoder buffer issue, the edited stream must ensure that the buffer would not overflow or underfiow as the result of the editing. This thesis invented a mathematical model that could describe the editing task and predict the editing effect on the buffer. Based on this model, the thesis then built a method to edit a stream safely. This method could produce an edited stream that maintained an MIPEG2 decoder\u27s buffer behavior as if the decoder were receiving a non-edited stream. In the segment extraction issue, the proper method to extract a segment from a source bitstream was discussed. The segment must be independent, that is, no coded picture in the segment would be associated with any other picture data outside of the segment. A proper extraction should also maintain the decoding order and display order of the picture sequence. This thesis visited several past studies on this issue, and proposed a practical method to perform segment extraction. Simulation results on the discussed methods were also presented

    Coding and Probabilistic Inference Methods for Data-Dependent Two-Dimensional Channels

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    Recent advances in magnetic recording systems, optical recording devices and flash memory drives necessitate to study two-dimensional (2-D) coding techniques for reliable storage/retrieval of information. Most channels in such systems introduce errors in messages in response to certain data patterns, and messages containing these patterns are more prone to errors than others. For example, in a single-level cell flash memory channel, inter-cell interference (ICI) is at its maximum when 101 patterns are programmed over adjacent cells in either horizontal or vertical directions. As another example, in two-dimensional magnetic recording channels, 2-D isolated-bits patterns are shown empirically to be the dominant error event, and during the read-back process inter-symbol interference (ISI) and inter-track interference (ITI) arise when these patterns are recorded over the magnetic medium. Shannon in his seminal work, ``A Mathematical Theory of Communications," presented two techniques for reliable transmission of messages over noisy channels, namely error correction coding and constrained coding. In the first method, messages are protected via an error correction code (ECC) from random errors which are independent of input data. The theory of ECCs is well studied, and efficient code construction methods are developed for simple binary channels, additive white Gaussian noise (AWGN) channels and partial response channels. On the other hand, constrained coding reduces the likelihood of corruption by removing problematic patterns before transmission over data-dependent channels. Prominent examples of constraints include a family of binary one-dimensional (1-D) and 2-D (d,k)\left(d,k\right)-run-length-limited (RLL) constraints which improves resilience to ISI timing recovery and synchronization for bandwidth limited partial response channels, where d and k represent the minimum and maximum number of admissible zeros between two successive ones in any direction of array. In principle, the ultimate coding approach for such data-dependent channels is to design a set of sufficiently distinct error correction codewords that also satisfy channel constraints. Designing channel codewords satisfying both ECC and channel constraints is important as it would achieve the channel capacity. However, in practice this is difficult, and we rely on sub-optimal methods such as forward concatenation method (standard concatenation), reverse concatenation method (modified concatenation), and combinations of these approaches. In this dissertation, we focus on the problem of reliable transmission of binary messages over data-dependent 2-D communication channels. Our work is concerned with several challenges in regard to the transmission of binary messages over data-dependent 2-D channels. Design of Two-Dimensional Magnetic Recording (TDMR) Detector and Decoder: TDMR achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in 2-D ISI and very high media noise. Therefore, it is critical to handle the media noise along with the 2-D ISI detection. In this work, we tune the Generalized Belief Propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. Investigation into Harmful Patterns for TDMR channels: This work investigates into the Voronoi based media model to study the harmful patterns over multi-track shingled recording systems. Through realistic quasi micromagnetic simulations studies, we identify 2-D data patterns that contribute to high media noise. We look into the generic Voronoi model and present our analysis on multi-track detection with constrained coded data. We show that 2-D constraints imposed on input patterns result in an order of magnitude improvement in the bit error rate for TDMR systems. Understanding of Constraint Gain for TDMR Channels: We study performance gains of constrained codes in TDMR channels using the notion of constraint gain. We consider Voronoi based TDMR channels with realistic grain, bit, track and magnetic-head dimensions. Specifically, we investigate the constraint gain for 2-D no-isolated-bits constraint over Voronoi based TDMR channels. We focus on schemes that employ the GBP algorithm for obtaining information rate estimates for TDMR channels. Design of Novel Constrained Coding Methods: In this work, we present a deliberate bit flipping (DBF) coding scheme for binary 2-D channels, where specific patterns in channel inputs are the significant cause of errors. The idea is to eliminate a constrained encoder and, instead, embed a constraint into an error correction codeword that is arranged into a 2-D array by deliberately flipping the bits that violate the constraint. The DBF method relies on the error correction capability of the code being used so that it should be able to correct both deliberate errors and channel errors. Therefore, it is crucial to flip minimum number of bits in order not to overburden the error correction decoder. We devise a constrained combinatorial formulation for minimizing the number of flipped bits for a given set of harmful patterns. The GBP algorithm is used to find an approximate solution for the problem. Devising Reduced Complexity Probabilistic Inference Methods: We propose a reduced complexity GBP that propagates messages in Log-Likelihood Ratio (LLR) domain. The key novelties of the proposed LLR-GBP are: (i) reduced fixed point precision for messages instead of computational complex floating point format, (ii) operations performed in logarithm domain, thus eliminating the need for multiplications and divisions, (iii) usage of message ratios that leads to simple hard decision mechanisms

    Fixed-Length Payload Encoding for Low-Jitter Controller Area Network Communication

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    The controller area network (CAN) bit stuffing mechanism, albeit essential to ensure proper receiver clock synchronization, introduces a significant, payload-dependent jitter on message response times, which may worsen the timing accuracy of a networked control system. Accordingly, several approaches to overcome this issue have been discussed in literature. This paper presents a novel software payload encoding scheme, which is able to guarantee that no stuff bits will ever be added to the data field by the CAN controller during transmission and, hence, lessens jitters considerably. Particular care has been put in its practical implementation and its subsequent evaluation to show how the simplicity and inherent high performance of the scheme make it suitable even for low-cost, embedded architectures

    LOZENGE TILING CONSTRAINED CODES

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    While the field of one-dimensional constrained codesis mature, with theoretical as well as practical aspects of codeanddecoder-design being well-established, such a theoreticaltreatment of its two-dimensional (2D) counterpart is still unavailable.Research has been conducted on a few exemplar2D constraints, e.g., the hard triangle model, run-length limitedconstraints on the square lattice, and 2D checkerboardconstraints. Excluding these results, 2D constrained systemsremain largely uncharacterized mathematically, with only loosebounds of capacities present. In this paper we present a lozengeconstraint on a regular triangular lattice and derive Shannonnoiseless capacity bounds. To estimate capacity of lozenge tilingwe make use of the bijection between the counting of lozengetiling and the counting of boxed plane partitions

    Block Pickard Models for Two-Dimensional Constraints

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