Decoding Time Research Articles

A Hybrid Check Polytope Projection Algorithm for ADMM Decoding of LDPC Codes

The Euclidean projection onto check polytope is the most complicated and time-consuming operation in the alternating direction method of multipliers (ADMM) decoding algorithm for low-density parity-check (LDPC) codes. Existing approximate even-vertex projection algorithm (EVA) can simplify the projection operation, but yields poor decoding performance. By alternately adopting EVA and other accurate projection algorithms, this letter proposes an innovative and simple hybrid projection algorithm (HPA) which can increase the percentage of unuseful projections. Simulation results show that the proposed algorithm can substantially reduce the projection time while achieving better frame error rate (FER) performance when compared with the standard ADMM decoding and the ADMM penalized decoding in the case of avoiding the tedious work of penalty parameter optimization. More importantly, compared with cut search algorithm (CSA), the proposed algorithm can significantly save the average projection time by nearly 80%, and the average decoding time by about 85%.

Deep Learning-Based Decoding of Linear Block Codes for Spin-Torque Transfer Magnetic Random Access Memory

Thanks to its superior features of fast read/write speed and low power consumption, spin-torque transfer magnetic random access memory (STT-MRAM) has become a promising non-volatile memory (NVM) technology that is suitable for many applications. However, the reliability of STT-MRAM is seriously affected by the variation of the memory fabrication process and the working temperature, and the later will lead to an unknown offset of the channel. Hence, there is a pressing need to develop more effective error correction coding techniques to tackle these imperfections and improve the reliability of STT-MRAM. In this work, we propose, for the first time, the application of deep-learning (DL)-based algorithms and techniques to improve the decoding performance of linear block codes with short codeword lengths for STT-MRAM. We formulate the belief propagation (BP) decoding of linear block code as a neural network (NN) and propose a novel neural normalized-offset reliability-based min-sum (RB-MS) (NNORB-MS) decoding algorithm. We successfully apply our proposed decoding algorithm to the STT-MRAM channel through channel symmetrization to overcome the channel asymmetry. We also propose an NN-based soft information generation method (SIGM) to take into account the unknown offset of the channel. Simulation results demonstrate that our proposed NNORB-MS decoding algorithm can achieve significant performance gain over both the hard-decision decoding (HDD) and the regular RB-MS decoding algorithm, for cases without and with the unknown channel offset. Moreover, the decoder structure and time complexity of the NNORB-MS algorithm remain similar to those of the regular RB-MS algorithm.

Bidirectional Viterbi decoding algorithm for OvTDM

Overlapped time domain multiplexing (OvTDM) is an innovative encoding scheme that can obtain high spectral efficiency. However, the intentional inter-symbol interference (ISI) caused by OvTDM will make the decoding process more complex. The computational complexity of maximum likelihood sequence detection increases exponentially with the growth of spectral efficiency in OvTDM. As a consequence of high complexity, the decoding effort for a given spectral efficiency may occasionally exceed the physical limitations of the decoder, leading inevitably to buffer overflows and information erasures. In this paper, we propose a bidirectional Viterbi algorithm (BVA) based on the bidirectional sequence decoding for OvTDM. With the BVA, the decoding operation starts simultaneously from the both ends of the corresponding trellis and stops at the middle of trellis. The simulation results show that compared with Viterbi algorithm (VA), the decoding time of BVA can be reduced by about half. And the memory space of two decoders in BVA are about half of that in VA, which means that the BVA has lower memory requirements for decoder. And the decoding performance of BVA is almost the same as VA.

Depth perception based on monochromatic shape encode-decode structured light method

This paper presents a depth perception system based on one-shot coding structured light method. The designed monochromatic pattern is robust against occlusion and sharp deformation, suitable for dynamic scene, and less sensitive for color interference, with symbol density spectrum (SDS) rule, legitimately increasing number of feature points inside element which further enhancing resolution. For the decoding stage, firstly, the proposed adaptive-symmetry-template (ASD) can robustly detect feature points, secondly, in virtue of transfer learning, using DenseNet to identify element label, thirdly, graph topological structure and look-up-table (LUT) help reducing decoding time. The experiments show that the encoding method increases resolution fairly a lot and for several complex objects, the decoding method can achieve high accuracy and keep robustness.

Fast DST-VII/DCT-VIII With Dual Implementation Support for Versatile Video Coding

The Joint Video Exploration Team (JVET) recently launched the standardization of the next-generation video coding named Versatile Video Coding (VVC) with the inherited technical framework from its predecessor High-Efficiency Video Coding (HEVC). The simplified Enhanced Multiple Transform (EMT) has been adopted as the primary residual coding transform solution, termed Multiple Transform Selection (MTS). In MTS, only the transform set consisting of DST-VII and DCT-VIII remains, excluding the other transform sets and the dependency on intra prediction modes. Significant coding gains are achieved by introducing new DST/DCT transforms, but the full matrix implementation is relatively costly compared to partial butterfly in terms of both software run-time and operation counts. In this work, we exploit the inherent features existing in DST-VII and DCT-VIII. Instead of repeating the element-wise additions and multiplications in full matrix operation, these features can be leveraged to achieve more efficient implementations which only use partial elements to derive the identical results. Existing transform matrices are further tuned to utilize these (anti-)symmetric features. A partial butterfly-type fast algorithm with dual-implementation support is proposed for DST-VII/DCT-VIII transform in VVC. Complexity analysis including operation counts and software run-time are conducted to validate the effectiveness. In addition, we prove the features are perfectly supported by theory. The proposed fast methods achieve noticeable software run-time savings without compromising on coding performance by comparing with the VVC Test Model VTM-3.0. It is shown that under Common Test Condition (CTC) with inter MTS enabled, an average of 9%, 0%, and 3% decoding time savings are achieved for All Intra (AI), Random Access (RA) and Low Delay B (LDB), respectively. Under low QP test condition with inter MTS enabled, the proposed fast methods achieve 1%, 2% and 4% decoding time savings on average for AI, RA, and LDB, respectively.

Dissimilation of synonymous codon usage bias in virus-host coevolution due to translational selection.

Eighteen of the 20 amino acids are each encoded by more than one synonymous codon. Due to differential transfer RNA supply within the cell, synonymous codons are not used with equal frequency, a phenomenon termed codon usage bias (CUB). Previous studies have demonstrated that CUB of endogenous genes trans-regulates the translational efficiency of other genes. We hypothesized similar effects for CUB of exogenous genes on host translation, and tested it in the case of viral infection, a common form of naturally occurring exogenous gene translation. We analysed public Ribo-Seq datasets from virus-infected yeast and human cells and showed that virus CUB trans-regulated tRNA availability, and therefore the relative decoding time of codons. Manipulative experiments in yeast using 37 synonymous fluorescent proteins confirmed that an exogenous gene with CUB more similar to that of the host would apply decreased translational load on the host per unit of expression, whereas expression of the exogenous gene was elevated. The combination of these two effects was that exogenous genes with CUB overly similar to that of the host severely impeded host translation. Finally, using a manually curated list of viruses and natural and symptomatic hosts, we found that virus CUB tended to be more similar to that of symptomatic hosts than that of natural hosts, supporting a general deleterious effect of excessive CUB similarity between virus and host. Our work revealed repulsion between virus and host CUBs when they are overly similar, a previously unrecognized complexity in the coevolution of virus and host.

ADMM penalized decoding method based on improved penalty function for LDPC codes in the IoTs

Low-density parity-check (LDPC) codes is widely used in communication technology which is the key technology of the internet of things (IoTs). By increasing the cost of the pseudocodewords, the alternating direction method of multipliers (ADMM) penalized decoding method improves the decoding performance for LDPC codes at low signal-to-noise ratios. In order to increase the ADMM penalized decoding speed, an improved penalty function, which combines the original l1 and l2 penalty functions to form a piecewise function for penalized decoding, is proposed in this letter. The simulation results show that compared with the original ADMM penalized decoding methods for LDPC codes, the improved method obtains better decoding performance and reduces the average decoding time.

Comparing Neural Network Based Decoders for the Surface Code

Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error correction codes can be efficiently decoded by employing machine learning techniques based on neural networks (NN). Various NN-based decoders have been proposed to enhance the decoding performance and the decoding time. Their implementation differs in how the decoding is performed, at logical or physical level, as well as in several neural network related parameters. In this work, we implement and compare two NN-based decoders, a low level decoder and a high level decoder, and study how different NN parameters affect their decoding performance and execution time. Crucial parameters such as the size of the training dataset, the structure and the type of the neural network, and the learning rate used during training are discussed. After performing this comparison, we conclude that the high level decoder based on a Recurrent NN shows a better balance between decoding performance and execution time and it is much easier to train. We then test its decoding performance for different code distances, probability datasets and under the depolarizing and circuit error models.

Efficient Design to Error Detection in EG-LDPC Codes

Capability to correct large number of errors Majority logic decodable codes is suitable for memory applications. The memory access time as well as area of utilization and the decoding time is reducing using Majority Logic Decoder. In Euclidean Geometry Low-Density Parity-Check (EG-LDPC) codes there exits fault secure detector which are used for error correction. Because EG-LDPC codes are widely used it will avoid high complexity in decoding. Majority logic decidable which is a one step is a technique similar to a class of Euclidean geometry low density parity check (EG-LDPC) codes. This method is very effective for EG-LDPC codes as shown in the result. The number of errors are detected by the majority logic decode further it can be used for correct the errors which occurred.

Statistical characterization of tile decoding time of HEVC-encoded 360° video

In this paper, we present a statistical characterization of tile decoding time of 360° videos encoded via HEVC that considers different tiling patterns and quality levels (i.e., bitrates). In particular, we present results for probability density function estimation of tile decoding time based on a series of experiments carried out over a set of 360° videos with different spatial and temporal characteristics. Additionally, we investigate the extent to which tile decoding time is correlated with tile bitrate (at chunk level), so that DASH-based video streaming can make possible use of such an information to infer tile decoding time. The results of this work may help in the design of queueing or control theory-based adaptive bitrate (ABR) algorithms for 360° video streaming.

Hardware-software implementation of HEVC decoder on Zynq

This paper presents an efficient implementation of the High Efficiency Video Coding (HEVC) decoder using Hardware/Software (HW/SW) co-design approach on the Zynq System on Chip (SoC) Platform. The reference software decoder HM 10.0 has been implemented under embedded Linux Operating System (OS). For real-time decoding, we provide hardware acceleration for the most computationally intensive parts of the HEVC decoder, which are the interpolation filters. The proposed design improves the processing throughput targeting on the resolution of 3840 × 2160 at a frame rate of 60 fps. HW/SW validation is achieved and examined in terms of resource utilization, throughput and power consumption. In order to improve the total decoding time, we propose to enable the Direct Memory Access (DMA) mode that can help speed page access and minimize the transfer time between the processor and hardware accelerators.

Subsampled Sum-Modified-Laplacian for Adaptive Loop Filter in Versatile Video Coding

This paper proposes a Subsampled Sum-Modified-Laplacian (SSML) operator for the block classification of the Adaptive Loop Filter (ALF) in Versatile Video Coding (VVC). The VVC Test Model (VTM)-2.0 includes Geometry transformation-based ALF (GALF) with $4\times 4$ block classification, a single $7\times 7$ Luma diamond-shaped filter, and spatial adaptation at the Coding Tree Block (CTB) level to improve the coding efficiency of VVC. However, in the $4\times 4$ block classification, 1-D (1-Dimensional) Modified-Laplacian (ML) values for various directions are calculated at all sample positions within an $8\times 8$ window, and these are summed to derive the gradients for the corresponding directions. For a CTB where the Wiener filter is applied, every $4\times 4$ Luma block within the CTB must calculate Sum-Modified-Laplacian (SML) operations, which results in increased computational complexity of the decoder. Therefore, four different subsampling patterns based on $4\times 4$ block classification for the SSML operator that calculate the 1-D ML values at subsampled sample positions within the $8\times 8$ window are proposed as attempts to reduce the computational complexity of the block classification process in the ALF of VTM-2.0. As all of the proposed subsampling patterns reduce the number of sample positions needed to calculate the 1-D ML operations by half, the experimental results demonstrate that the proposed method achieves total decoding time reductions in the range of 2% to 3%, while the coding gain of the ALF is maintained.

A Generalized Parity-Check Transformation for Iterative Soft-Decision Decoding of Binary Cyclic Codes

The conventional parity-check matrix transformation algorithm (PTA) requires matrix inversion to transform matrices of maximum distance separable (MDS) codes. However, such matrix transformation is not always guaranteed for the class of non-MDS codes. Hence, the PTA fails for binary cyclic (BC) codes. To overcome this limitation, we developed a generalized parity-check matrix transformation (GPT) algorithm for binary cyclic codes. The GPT avoids the matrix inversion step of the PTA. It permutes the columns of the parity-check matrix based on the reliability information from the channel. Results show a significant bit error rate (BER) performance gain as compared to the existing PTA. It also presents a reasonable BER performance as compared to the other soft-decision (SD) decoding algorithms. In addition, the decoder functions within a practical decoding time complexity, particularly at the message passing stage.

A multivariate investigation of visual word, face, and ensemble processing: Perspectives from EEG-based decoding and feature selection.

Recent investigations have focused on the spatiotemporal dynamics of visual recognition by appealing to pattern analysis of EEG signals. While this work has established the ability to decode identity-level information (such as the identity of a face or of a word) from neural signals, much less is known about the precise nature of the signals that support such feats, their robustness across visual categories, or their consistency across human participants. Here, we address these questions through the use of EEG-based decoding and multivariate feature selection as applied to three visual categories: words, faces and face ensembles (i.e., crowds of faces). Specifically, we use recursive feature elimination to estimate the diagnosticity of time and frequency-based EEG features for identity-level decoding across three datasets targeting each of the three categories. We then relate feature diagnosticity across categories and across participants while, also, aiming to increase decoding performance and reliability. Our investigation shows that word and face processing are similar in their reliance on spatiotemporal information provided by occipitotemporal channels. In contrast, ensemble processing appears to also rely on central channels and exhibits a similar profile with word processing in the frequency domain. Further, we find that feature diagnosticity is stable across participants and is even capable of supporting cross-participant feature selection, as demonstrated by systematic boosts in decoding accuracy and feature reduction. Thus, our investigation sheds new light on the nature and the structure of the information underlying identity-level visual processing as well as on its generality across categories and participants.

Latent Attribute Based Hierarchical Decoder for Neural Machine Translation

Neural machine translation (NMT) has achieved state-of-the-art performance in many translation tasks. However, because the computational cost increases with the size of the search space for predicting the target words, the translation quality of NMT is constrained by the limited vocabulary. To alleviate this problem, we propose a novel dynamic hierarchical decoder for NMT to utilize all of the target words in the training and decoding process. In the proposed model, a target word is represented by two latent attribute vectors rather than a word vector. The model is trained to dynamically put together those words that share similar linguistic attributes. The prediction of a target word is, therefore, turned into the prediction of attribute vectors, where the $\mathrm{softmax}$ functions are performed at the attribute level. This greatly reduces the model size and the decoding time. Our experimental results demonstrate that the proposed model significantly outperforms the NMT baselines in both Chinese-English and English-German translation tasks.

Analysis of the upper bound of error correction time for the data protection with the use of the Reed-Solomon codes

This scientific paper deals with the error-correcting coding for the data protection and analysis of the upper bound of error correction time on application of the Reed-Solomon codes. The data encoding and decoding algorithms for the Reed-Solomon codes are briefly overviewed. The obtained by the author analytical formula for estimation of the upper bound of error correction time for the given size of data frames and multiplicity of correctable errors is presented. Finally, results of the experimental research of frame decoding and error correction time are also discussed.

Efficient Prediction Methods With Enhanced Spatial-Temporal Correlation for HEVC

The state-of-the-art high efficiency video coding (HEVC) standard provides a significant improvement relative to H.264/AVC, with almost 50% bitrate reduction. However, there are still requirements to further improve the coding performance of HEVC. In this paper, we reveal some problems in both intra and inter prediction by giving detailed observations, and solve them by further exploring spatial and temporal correlations. First, it is noteworthy that large boundary distortions in intra prediction blocks arise from the simplistic extrapolation operation with partial reference samples. To reduce these boundary distortions, we propose an enhanced prediction boundary filtering method based on generalized Gaussian distribution functions, and extend this method to merge prediction. Second, we combine intra and inter prediction to create a novel prediction method, in which spatial and temporal correlations are simultaneously considered. In addition, the existing merge mode can only provide accurate prediction results for simple movements, owing to the lack of motion estimation. To improve the flexibility, we optimize the existing merge mode by intensifying the temporal correlation with a simple motion search process. The experimental results show that the proposed methods achieve consistent bitrate savings, 1.3% for all intra, 1.5% for random access, 2.5% (up to 4.2%) for low delay P, and 1.2% for low delay B configuration. The encoding time only rises by 70% - 80%, and the decoding time increases negligibly.

Extended Coding Unit Partitioning for Future Video Coding.

The flexible partitioning structure such as quad-tree plus binary-tree (QTBT) plays important roles in the next generation video coding standard. This paper investigates new coding unit (CU) partitioning methods to further improve the compression efficiency. In particular, we propose the extended quad-tree partitioning and asymmetric ternary-tree partitioning to further develop the principle behind the existing partitioning structure. First, a central extended quad-tree (CENTRAL-EQT) partitioning is introduced, which extends the traditional QT partitioning with central pattern and generates four elaborately designed sub-CUs with different sizes. Second, we propose a parallel extended quad-tree (PARALLEL-EQT) partitioning that allows the CU to split along a single direction, leading to four identical size sub-blocks. Third, we present an asymmetric ternary-tree (ATT) method, which splits the CU asymmetrically into three sub-blocks. The proposed new partitioning methods allow the interleaving with binary-tree partitioning for enhanced adaptability, and they can be jointly enabled to capture different characteristics of the local content. Simulation results on the JEM7-QTBT-Only platform show that averagely 4.92%, 4.81% and 5.08% BD-Rate savings are achieved for the luma component by the proposed methods under random access (RA), low-delay P (LDP) and low-delay B (LDB) configurations, respectively, with around 725% encoding time increase and negligible decoding time increase. Furthermore, experimental results also reveal that the proposed partitioning schemes are effective when cooperating with the upcoming Versatile Video Coding standard.

Modelling LSUTE: PKE Schemes for Safeguarding Electronic Healthcare Records Over Cloud Communication Environment

Presently offering safety to the information stored in a cloud environment is a crucial and demanding process. For fulfilling various confidentiality safeguarding and encoding schemes several schemes are modelled but they comprise setbacks such as increased cost, increased time and poor safety levels. For addressing the setbacks the goal is to model a fresh scheme termed as layer screened unsigned tree based encoding (LSUTE) and position based keyword enquiry for offering confidentiality to the information stored into the cloud environment. Here the confidentiality is offered to the healthcare-related information uploaded into the electronic healthcare comprising two components for safeguarding the information storage and position based keyword exploration. Initially, the healthcare documents from the physician and lab technicians are stored in an encoded manner employing the modelled LSUTE scheme where only the trusted and verified individual can make use of the healthcare reports. Followed by which the position based exploration is performed based on the keyword and queries. A method based on decision trees is proposed to selectively decide whether to test all inter modes. The key merit is offering improved confidentiality to the healthcare information in a safe manner. The outcomes of the analyses reveal that the performance of the modelled schemes in terms of assessment cost, transmission cost, assessing queries, time of encoding, time of decoding and query reply time with negligible impact on coding efficiency. Therefore, algorithm to reduce the encoder run time with limited effects on the standard coding efficiency is implemented.

Lossless Image Compression Techniques: A State-of-the-Art Survey

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.