Fixed-length Coding Research Articles

Modified Joint Source–Channel Trellises for Transmitting JPEG Images over Wireless Networks

This paper presents a joint source–channel image transmission model based on a modified trellis construction using variable-length and fixed-length codes. The model employs linear block trellis codes and a modified Bahl–Cocke–Jelinek–Raviv algorithm for decoding in the source channel. Additionally, this model utilizes a hierarchical tree and a parity-check matrix to combine the source and channel codes, reducing errors in the decoding process and enabling the decoding of longer sequences without substantial loss of quality.

Equivalences among Some Information Measures for Individual Sequences and Their Applications for Fixed-Length Coding Problems

Equivalences among Some Information Measures for Individual Sequences and Their Applications for Fixed-Length Coding Problems

Метод кадрової синхронізації на основі кортежів попарно відмінних елементів

The further development of the frame synchronization method is presented, which uses as a synchronization word the permutation of the elements of the set of integers of the segment [0; M  1], was further developed. It is proposed to use a tuple of M-η pairwise distinct elements of the set of integers of the segment [0; M 1] as a syncword. The elements of this set are encoded with a fixed-length binary code and the minimum binary Hamming distance between the syncword and all its circular shifts is the maximum. The paper established that the maximum value of the minimum Hamming distance for tuples of 15 pairwise distinct elements of the set of integers for M = 16 is equal to 30. A comparative assessment of the frame synchronization effectiveness was performed based on tuples of 15 elements, as well as on permutations of length 16 and 8. A computer simulation model of the frame synchronization system in a binary symmetric communication channel was built. Synchronization indicators were determined with parameters calculated for bit error probability 0,4 and 0,495, as well as requirements for a min-imum probability of correct synchronization of 0,9997 and a maximum probability of false synchronization of 3E-4. The effectiveness of using tuples of pairwise distinct elements in frame synchronization systems has been confirmed. The efficiency indicator depends on com-munication channel bit error probability.

MAFH: Multilabel aware framework for bit-scalable cross-modal hashing

Deep hashing techniques have received considerable attention and are widely used in the field of cross-modal retrieval. The training mode of deep cross-modal hashing models has also gradually converged. However, the commonly used training mode ignores noisy labels from different modality original datasets and can only generate fixed-length hash codes, resulting in poor robustness and flexibility of the training generated hash models. Therefore, to effectively optimize the training mode, we propose a multilabel aware framework for bit-scalable deep cross-modal hashing (MAFH) with the following contributions. First, we introduce a label network module to indirectly used the hash representation of multilabels to more accurately supervise the model training process across all modalities. Second, we design a scalable coding strategy to freely manipulate the length of the hash code, which makes the trained model flexible to adapt to different scenarios. Third, we propose a layered multilabel similarity algorithm to preserve more complete feature information, which avoids the loss of discriminative feature information during model training. The training mode of MAFH can effectively enhance model robustness, flexibility and accuracy. We selected thirteen representative hash methods to compare with the proposed method, and experimental results on four public datasets show that the proposed method achieves good performance. https://github.com/x-28/MAFH.git.

Memory-Efficient Fixed-Length Representation of Synchronous Event Frames for Very-Low-Power Chip Integration

The new event cameras are now widely used in many computer vision applications. Their high raw data bitrate levels require a more efficient fixed-length representation for low-bandwidth transmission from the event sensor to the processing chip. A novel low-complexity lossless compression framework is proposed for encoding the synchronous event frames (EFs) by introducing a novel memory-efficient fixed-length representation suitable for hardware implementation in the very-low-power (VLP) event-processing chip. A first contribution proposes an improved representation of the ternary frames using pixel-group frame partitioning and symbol remapping. Another contribution proposes a novel low-complexity memory-efficient fixed-length representation using multi-level lookup tables (LUTs). Complex experimental analysis is performed using a set of group-size configurations. For very-large group-size configurations, an improved representation is proposed using a mask-LUT structure. The experimental evaluation on a public dataset demonstrates that the proposed fixed-length coding framework provides at least two times the compression ratio relative to the raw EF representation and a close performance compared with variable-length video coding standards and variable-length state-of-the-art image codecs for lossless compression of ternary EFs generated at frequencies bellow one KHz. To our knowledge, the paper is the first to introduce a low-complexity memory-efficient fixed-length representation for lossless compression of synchronous EFs, suitable for integration into a VLP event-processing chip.

Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code

As the demands of multimedia and data services increase, efficient communication systems are being investigated to meet the high data rate requirements. Joint source-channel coding (JSCC) schemes were proposed for improving overall system performance. However, existing JSCC systems may suffer a symbol error rate (SER) performance loss when residual source redundancy is not fully exploited. This paper presents a novel, low-complexity JSCC system, which consists of a fixed-length source block code and an irregular convolutional channel code. A simple approach is proposed to design source codes that minimize the SER of source detection and guarantee the convergence of iterative source-channel decoding (ISCD). To improve the waterfall performance of ISCD, the channel code is optimized by using the extrinsic information transfer (EXIT) chart and the concept of irregular code. The channel code is constituted by recursive non-systematic convolutional (RNSC) subcodes. The weights of subcodes are optimized to make the EXIT curves of the channel decoder and the source decoder well-matched, and therefore, a near-capacity performance is achieved. Simulation results show that the proposed system achieves more than 1 dB gains and 0.3 dB gains compared to the separate source-channel code system and the other optimal JSCC systems, respectively. Additionally, the performance of the proposed system is within 1 dB deviation from the Shannon limit capacity.

MTS-Stega: Linguistic Steganography Based on Multi-Time-Step.

Generative linguistic steganography encodes candidate words with conditional probability when generating text by language model, and then, it selects the corresponding candidate words to output according to the confidential message to be embedded, thereby generating steganographic text. The encoding techniques currently used in generative text steganography fall into two categories: fixed-length coding and variable-length coding. Because of the simplicity of coding and decoding and the small computational overhead, fixed-length coding is more suitable for resource-constrained environments. However, the conventional text steganography mode selects and outputs a word at one time step, which is highly susceptible to the influence of confidential information and thus may select words that do not match the statistical distribution of the training text, reducing the quality and concealment of the generated text. In this paper, we inherit the decoding advantages of fixed-length coding, focus on solving the problems of existing steganography methods, and propose a multi-time-step-based steganography method, which integrates multiple time steps to select words that can carry secret information and fit the statistical distribution, thus effectively improving the text quality. In the experimental part, we choose the GPT-2 language model to generate the text, and both theoretical analysis and experiments prove the effectiveness of the proposed scheme.

Effective Anomaly Space for Hyperspectral Anomaly Detection

Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a crucial factor in anomaly detection (AD). The difficulty with dealing with BS arises from the fact that anomalies are generally sandwiched between background (BKG) and noise. This paper presents a new concept, called effective anomaly space (EAS) to resolve this dilemma. To accomplish this goal, the well-known independent component analysis (ICA) is used to address the between BKG and anomalies issue by removing the first two orders of data statistics (2OS), while sparsity cardinality (SC) is used to address the between anomalies and noise issue by removing non-Gaussian noises and interferers from anomalies. Specifically, SC is re-derived as fixed SC (FSC) for a spectral vector and a spatial band corresponding to fixed length coding and variable SC (VSC) for a spectral-spatial sample and spatial-spectral band corresponding to variable length coding from information theory. Combining ICA and the new versions of SC allows EAS not only to remove BKG-characterized by 2OS including Gaussian-distributed signal sources, but also to remove non-Gaussian noises/interferers from anomalies. As a result, EAS can significantly increase anomaly detectability. In particular, one of great benefits resulting from EAS is that EAS can also improve current low rank and sparse representation (LRaSR)-based methods used for AD.

Utilization of huffman coding for weather buoy system using long-range communication

Weather buoy is an instrument used to gather weather and ocean parameters and is deployed in the world’s ocean. Monitoring sensors are used for gathering weather and ocean parameters. The data is transmitted through long-range (LoRa) communication system. Transmission in a weather buoy is conventional by sending non-compressed input data, or via digital signal processing that compresses and decompresses the gathered data in a real-time transmission. In this paper, the Huffman Coding algorithm was used for minimizing and compressing codes. It was utilized to ensure the transmission of digital data from the transmitter of the weather buoy to the receiver part. The fixed-length codes with different bit numbers generated by the transmitter reduced the length of the messages from an average of 2000 bits per second to 480 bits per second per message through the use of the Huffman Coding algorithm. The evaluated result consists of 125 transmissions and encountered 7 non-compressed data. Therefore, the text file compression or the accuracy is 118/125 or 94.4%. The Huffman Coding approach was found to be an optimal solution for data transfer as it can transmit compressed input data. It is about 13 seconds compared to a 6-minute time delay with occurrences of data traffic. It is for the data which are non-compressed and transmitted as it is. It shows significant improvement in the real-time transmission and monitoring, especially for weather system.

Cross-modal variable-length hashing based on hierarchy

Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of existing hashing methods use fixed-length hash codes, which increase the computational costs for large-size datasets. Furthermore, learning hash functions is an NP hard problem. To address these problems, we initially propose a novel method named Cross-modal Variable-length Hashing Based on Hierarchy (CVHH), which can learn the hash functions more accurately to improve retrieval performance, and also reduce the computational costs and training time. The main contributions of CVHH are: (1) We propose a variable-length hashing algorithm to improve the algorithm performance; (2) We apply the hierarchical architecture to effectively reduce the computational costs and training time. To validate the effectiveness of CVHH, our extensive experimental results show the superior performance compared with recent state-of-the-art cross-modal methods on three benchmark datasets, WIKI, NUS-WIDE and MIRFlickr.

Variable-length image compression based on controllable learning network

While neural-network-based lossy image compression methods have shown impressive performance, most of them output a fixed-length coding using a trained-specific network. However, it is essential to support the variable-length compression or meet a target rate with a high-coding performance in practice. This paper steps forward the neural-network-based image compression method, making it possible for a single network model to generate variable compression rates. Our network model combines an auto-encoder (AE) and a generative adversarial network (GAN) for generative compression. We introduce a noise interference mechanism to train the feature representation produced by the encoder, making the feature nodes training controllable and distributed from top to bottom according to their importance in feature expression. Based on this importance distribution, the latent nodes are quantized into bits and the variable-length compression can be achieved by discarding bits of those less-important feature nodes to meet the compression target. We propose several noise interference methods, and the experiments confirm the feasibility of method Random-add and Dropout in controllable learning. Further experiments illustrate that our compression method can not only achieve variable-length compression but also can recover high-quality compressed images at extremely low bit rates, outperforming that with a fixed rate.

Entropy Optimized Deep Feature Compression

This letter focuses on the compression of deep features. With the rapid expansion of deep feature data in various CNN-based analysis and processing tasks, the demand for efficient compression continues to increase. Product quantization (PQ) is widely used in the compact expression of features. In the quantization process, feature vectors are mapped into fixed-length codes based on a pre-trained codebook. However, PQ is not specifically designed for data compression, and the fixed-length codes are not suitable for further compression such as entropy coding. In this letter, we propose an entropy-optimized compression scheme for deep features. By introducing entropy into the loss function in the training process of quantization, the quantization and entropy coding modules are jointly optimized to minimize the total coding cost. We evaluate the proposed methods in retrieval tasks. Compared with fixed-length coding, the proposed scheme can be generally combined with PQ and its extended method and can achieve a better compression performance consistently.

A Novel Dynamic Clustering Method by Integrating Marine Predators Algorithm and Particle Swarm Optimization Algorithm

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching Algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.

On Exact Asymptotics of the Error Probability in Channel Coding: Symmetric Channels

The exact order of the optimal sub-exponentially decaying factor in the classical bounds on the error probability of fixed-length codes over a Gallager-symmetric discrete memoryless channel with and without ideal feedback is determined for rates above the critical rate. Regardless of the availability of feedback, it is shown that the order of the optimal sub-exponential factor exhibits a dichotomy. Moreover, the proof technique is used to establish the third-order term in the normal approximation for symmetric channels, where a similar dichotomy is shown to exist.

Ordered and fixed‐length bit‐string fingerprint representation with minutia vicinity combined feature and spectral clustering

The minutiae set defined by the ISO/IEC 19794-2 is one of the prevalent feature used in fingerprint recognition systems. Unfortunately, such characteristic of unordered and variable-sized minutiae information causes a restriction on the operation in some advanced template protection methods (e.g. fuzzy commitment), which usually require an ordered and fixed-length binary feature representation as the system input. In this study, in order to simultaneously extend the application of fingerprint recognition and provide satisfactory system performance, the authors propose a novel fixed-length bit-string conversion framework based on spectral clustering and the proposed newly designed discriminative fingerprint representation called minutia vicinity combined feature (MVCF). The proposed method consists of three stages: (i) the extraction of MVCF, (ii) bit conversion via the spectral clustering algorithm, and (iii) matching. Benefiting from feature invariance, fixed-length and bit-oriented coding, merits such as fast matching and decent accuracy are well guaranteed. The performance evaluation is conducted on six publicly available benchmark data sets: FVC2002 DB1, DB2, DB3 and FVC2004 DB1, DB2, DB3 confirms the superiority of the proposed method and suggests the promise of migrating to some other domains (e.g., template protection).

Distributed Hypothesis Testing With Variable-Length Coding

The problem of distributed testing against independence with variable-length coding is considered when the average and not the maximum communication load is constrained as in previous works. The paper characterizes the optimum type-II error exponent of a single-sensor single-decision center system given a maximum type-I error probability when communication is either over a noise-free rate-R link or over a noisy discrete memoryless channel (DMC) with stop-feedback. Specifically, let E denote the maximum allowed type-I error probability. Then the optimum exponent of the system with a rate-R link under a constraint on the average communication load coincides with the optimum exponent of such a system with a rate R/(1-ε) link under a maximum communication load constraint. A strong converse thus does not hold under an average communication load constraint. A similar observation also holds for testing against independence over DMCs. With variable-length coding and stopfeedback and under an average communication load constraint, the optimum type-II error exponent over a DMC of capacity C equals the optimum exponent under fixed-length coding and a maximum communication load constraint when communication is over a DMC of capacity C/(1 - ε).

Short-Packet Transmission via Variable-Length Codes in the Presence of Noisy Stop Feedback

We present an upper bound on the error probability achievable using variable-length stop feedback (VLSF) codes, for a fixed size of the information payload and a given constraint on the maximum latency and the average service time. Differently from the bound proposed in Polyanskiy et al. (2011), which pertains to the scenario in which the stop signal is sent over a noiseless feedback channel, our bound applies to the practically relevant setup in which the feedback link is noisy. Numerical evaluation of our bound suggests that, for fixed latency and reliability constraints, noise in the feedback link may increase the minimum average service time for the VLSF scheme considered in this paper, to the extent that fixed-length codes without feedback may be preferable in some scenarios.

Variants of Golomb Coding and the n-ary Versions

Golomb coding is a type of entropy encoding scheme for geometric distributions. It consists of two parts, and both parts are coded with variable-length coding, which requires a higher computational effort than fixed-length coding schemes. To solve this issue, the first part of this article presents a variant of Golomb coding that uses fixed-length coding to code the first part. The simulations show that the proposed coding scheme has a higher throughput than Golomb coding, due to the reduction of arithmetic complexity. In the second part, we discuss the $n$ -ary versions of Golomb coding and the proposed coding scheme.

An efficient machine tool control instruction compression method for networked numerical control systems

With the development of computer and information technology, function modules of computer numerical control (CNC) systems are moving up to network environment to simplify local machine tool controller architecture and increase function reusability. In this context, network-based CNC system generates machine control instructions (MCI) in network environment and send MCI to local machines. Compared with building a remote real-time communication channel between networked CNC and local machines, applying offline interpolator is more feasible under state-of-the-art hardware and software. However, the size of MCI generated by offline interpolator is large. Transmitting MCI without compression will be inefficient and increase lead time. Currently there has been few researches on the features and compression of offline MCI. In this paper, a simple and effective compression method for MCI named multichannel differential run-length encoding (MDRL) is proposed. Utilizing the limited variation and high repetition features of MCI, MDRL algorithm first calculates the differences of MCI, then encodes the amplitudes and repetition of difference values using fixed length codes. Compression performance discrepancy among different channels is controlled using compressed data chunks. The decompression process of MDRL only needs a few hundred of bytes of space and can process in real-time. Compression performance of MDRL is tested using three common manufacturing tasks under different system configurations. The results show that the mean compression ratio of MDRL is 3.41%-15.86%. The advantages of MDRL are further validated by comparing with three other data compression algorithms. By using MDRL, MCI transmission efficiency can be significantly increased for networked CNC systems to increase function reusability and shorten lead time.

An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

This paper presents a novel iris recognition system based on machine learning methods. The motivation behind this research resides in the interrelatedness of biometric systems and stylometry, as shown in our previous research. The main goal of the proposed model is to reach virtually perfect classification accuracy, eliminate false acceptance rates, and cancel the possibility of recreating an iris image from a generated template. To achieve this, we omit Gabor wavelets and other filter banks typically employed in iris recognition systems based on the pioneering work of John Daugman. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting a normalized iris image into a one-dimensional set of fixed-length codes, which then undergoes stylometric feature extraction. The extracted features are further used for classification. A new recognition method is developed using the CASIA iris database, and its generalizability is demonstrated on the MMU and IITD iris databases separately, and also on their unification with the CASIA database, by applying oversampling before and during the cross-validation procedure. The experimental evaluation shows that the system performs as intended. In addition, the computational costs are significantly decreased with respect to traditional systems, which in turn reduces the overall complexity of the recognition system, making it suitable for use in practical applications.