Bit-plane encoding - A technique for source encoding.
Bit-plane encoding technique used for reducing redundancy in data gathered in space probes
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Bit-plane encoding technique used for reducing redundancy in data gathered in space probes
Error correction coding/decoding techniques integration, discussing convolutional and block coding and Viterbi, Sequential and Threshold decoding algorithms
Some non-Fourier encoding methods such as wavelet and direct encoding use spatially localized bases. The spatial localization feature of these methods enables optimized encoding for improved spatial and temporal resolution during dynamically adaptive MR imaging. These spatially localized bases, however, have inherently reduced image signal-to-noise ratio compared with Fourier or Hadamad encoding for proton imaging. Hyperpolarized noble gases, on the other hand, have quite different MR properties compared to proton, primarily the nonrenewability of the signal. It could be expected, therefore, that the characteristics of image SNR with respect to encoding method will also be very different from hyperpolarized noble gas MRI compared to proton MRI. In this article, hyperpolarized noble gas image SNRs of different encoding methods are compared theoretically using a matrix description of the encoding process. It is shown that image SNR for hyperpolarized noble gas imaging is maximized for any orthonormal encoding method. Methods are then proposed for designing RF pulses to achieve normalized encoding profiles using Fourier, Hadamard, wavelet, and direct encoding methods for hyperpolarized noble gases. Theoretical results are confirmed with hyperpolarized noble gas MRI experiments. Copyright 2001 Academic Press.
Image storage requirements for several encoding methods are investigated and the use of quadtrees with multigray level or multicolor images are explored. The results of encoding a variety of images having up to 256 gray levels using three schemes (full raster, runlength and quadtree) are presented. Although there is considerable literature on the use of quadtrees to store and manipulate binary images, their application to multilevel images is relatively undeveloped. The potential advantage of quadtree encoding is that an entire area with a uniform gray level may be encoded as a unit. A pointerless quadtree encoding scheme is described. Data are presented on the size of the quadtree required to encode selected images and on the relative storage requirements of the three encoding schemes. A segmentation scheme based on the statistical variation of gray levels within a quadtree quadrant is described. This parametric scheme may be used to control the storage required by an encoded image and to preprocess a scene for feature identification. Several sets of black and white and pseudocolor images obtained by varying the segmentation parameter are shown.
An optically coupled pressure altitude encoder which can be incorporated into commercially available inexpensive general aviation altimeters was successfully developed. The encoding of pressure altitude is accomplished in 100-ft (30.48-m) increments from -1000 to 20,000ft (-304.8 to 6096 m). The prototype encoders were retrofitted into two different internal altimeter configurations. A prototype encoder was checked for accuracy of transition points and environmental effects. Each altimeter configuration, with the encoder incorporated, was laboratory tested for performance and was subsequently flight-tested over the specified altitude range. With few exceptions, the assembled altimeter-encoder met aeronautical standards for altimeters and encoders. Design changes are suggested to improve performance to meet required standards consistently.
An alternative approach has been devised for encoding image data in compliance with JPEG 2000, the most recent still-image data-compression standard of the Joint Photographic Experts Group. Heretofore, JPEG 2000 encoding has been implemented by several related schemes classified as rate-based distortion-minimization encoding. In each of these schemes, the end user specifies a desired bit rate and the encoding algorithm strives to attain that rate while minimizing a mean squared error (MSE). While rate-based distortion minimization is appropriate for transmitting data over a limited-bandwidth channel, it is not the best approach for applications in which the perceptual quality of reconstructed images is a major consideration. A better approach for such applications is the present alternative one, denoted perceptual distortion control, in which the encoding algorithm strives to compress data to the lowest bit rate that yields at least a specified level of perceptual image quality. Some additional background information on JPEG 2000 is prerequisite to a meaningful summary of JPEG encoding with perceptual distortion control. The JPEG 2000 encoding process includes two subprocesses known as tier-1 and tier-2 coding. In order to minimize the MSE for the desired bit rate, a rate-distortion- optimization subprocess is introduced between the tier-1 and tier-2 subprocesses. In tier-1 coding, each coding block is independently bit-plane coded from the most-significant-bit (MSB) plane to the least-significant-bit (LSB) plane, using three coding passes (except for the MSB plane, which is coded using only one "clean up" coding pass). For M bit planes, this subprocess involves a total number of (3M - 2) coding passes. An embedded bit stream is then generated for each coding block. Information on the reduction in distortion and the increase in the bit rate associated with each coding pass is collected. This information is then used in a rate-control procedure to determine the contribution of each coding block to the output compressed bit stream.
Formal behavioral specifications written early in the system-design process and communicated across all design phases have been shown to increase the efficiency, consistency, and quality of the system under development. To prevent introducing design or verification errors, it is crucial to test specifications for satisfiability. Our focus here is on specifications expressed in linear temporal logic (LTL). We introduce a novel encoding of symbolic transition-based Buchi automata and a novel, "sloppy," transition encoding, both of which result in improved scalability. We also define novel BDD variable orders based on tree decomposition of formula parse trees. We describe and extensively test a new multi-encoding approach utilizing these novel encoding techniques to create 30 encoding variations. We show that our novel encodings translate to significant, sometimes exponential, improvement over the current standard encoding for symbolic LTL satisfiability checking.
Disclosed are a method and system for video compression, wherein the video encoder has low computational complexity and high compression efficiency. The disclosed system comprises a video encoder and a video decoder, wherein the method for encoding includes the steps of converting a source frame into a space-frequency representation; estimating conditional statistics of at least one vector of space-frequency coefficients; estimating encoding rates based on the said conditional statistics; and applying Slepian-Wolf codes with the said computed encoding rates. The preferred method for decoding includes the steps of; generating a side-information vector of frequency coefficients based on previously decoded source data, encoder statistics, and previous reconstructions of the source frequency vector; and performing Slepian-Wolf decoding of at least one source frequency vector based on the generated side-information, the Slepian-Wolf code bits and the encoder statistics.
Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
A flexible onboard encoder system was designed for the space shuttle. The following areas were covered: (1) implementation of the encoder design into hardware to demonstrate the various encoding algorithms/code formats, (2) modulation techniques in a single hardware package to maintain comparable reliability and link integrity of the existing link systems and to integrate the various techniques into a single design using current technology. The primary function of the command encoder is to accept input commands, generated either locally onboard the space shuttle or remotely from the ground, format and encode the commands in accordance with the payload input requirements and appropriately modulate a subcarrier for transmission by the baseband RF modulator. The following information was provided: command encoder system design, brassboard hardware design, test set hardware and system packaging, and software.
This paper presents the initial results of our study of the use of adaptive delta modulation to encode color signals. The techniques considered include direct encoding of the color video signal, encoding of each of the three color components, i.e., R, G and B, and encoding of the chrominance and luminance signals I, Y and Q. It is shown that at any transmitted bit rate between 8 Mb/s and 24 Mb/s the best quality picture reproduction occurs when the I, Y and Q signals are encoded and the poorest quality picture reproduction occurs when the composite video signal is encoded. Furthermore, the differences in quality are significant.
The logic structure of a universal VLSI chip called the symbol-slice Reed-Solomon (RS) encoder chip is discussed. An RS encoder can be constructed by cascading and properly interconnecting a group of such VLSI chips. As a design example, it is shown that a (255,223) RD encoder requiring around 40 discrete CMOS ICs may be replaced by an RS encoder consisting of four identical interconnected VLSI RS encoder chips. Besides the size advantage, the VLSI RS encoder also has the potential advantages of requiring less power and having a higher reliability.
Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
Block encoding of discrete, memoryless sources with equiprobable outputs subject to a fidelity criterion is discussed. The encoder is assumed to be deterministic and a lower bound is derived on the amount of computational work required to encode such sources as a function of the code rate and fidelity of the encoding. An example is given to show that the bound cannot be substantially improved. These bounds apply primarily to sources and distortion measures which are such that the minimum distortion given by the rate-distortion bound is not achievable with a finite block length, deterministic encoder.
Consideration of the transmission and detection of differentially encoded multiple phase-shift-keyed (MPSK) signals, paying particular attention to the ambiguity resolution problem resulting from suppression of the transmitted carrier. A study is made of the coherent detection of differentially encoded MPSK signals, and the performance of a differentially encoded MPSK system is compared with that of a system which transmits absolutely encoded polyphase signals and performs perfect ambiguity resolution. Both the perfect and noisy reference signal cases are treated. Also, the performance of coherent detection of differentially encoded MPSK signals is compared with that of differentially coherent reception of the same signal set.
This paper describes a command encoding scheme which was conceived to provide the necessary error protection for digital commands from a ground-based terminal to a manned spacecraft. The selected approach is compatible with a time-division multiplexing scheme which combines the command channel with two digital voice channels prior to transmission over the RF link. The command encoding scheme is also fully compatible with a channel encoding strategy which is applied to the multiplexed bit sequence (commands plus voice) to provide increased link efficiency. Implementation aspects of the command encoder and decoder are discussed and the system end-to-end performance improvement provided by the command encoding scheme is calculated.
The design and realization of digital devices which add or multiply delta modulation (DM) encoded signals are presented. These systems operate directly on the DM bit stream. It is shown that the devices can be constructed using standard digital hardware and that the hardware complexity needed to add or multiply the two DM encoded signals is equivalent to that needed for pulse code modulation (PCM) signals. Experimental results are presented showing the operation of these systems. The results obtained by adding or multiplying DM encoded signals are compared with those obtained using PCM encoded signals on the basis of signal-to-noise ratio (SNR). A spectral-analysis technique applicable to a DM encoded signal is developed to obtain SNR curves.
Research work is described that was carried out to develop adaptive delta modulators (ADM) capable of encoding black-and-white and color video signals in the presence of high bit error rates. These were a one-dimensional ADM capable of encoding black-and-white pictures and transmitting digitally encoded voice without increase in bandwidth; an ADM capable of encoding color video signals; and an ADM capable of encoding field-sequential color. For each of these three units, the bit rate could be adjusted within the range from 6 to 24 Mbits/sec. The effects of the bit rate and channel errors on the response of the picture are examined.