Low-complexity Encoding Research Articles

Binary cyclic-gap constant weight codes with low-complexity encoding and decoding

Binary cyclic-gap constant weight codes with low-complexity encoding and decoding

Low Complexity Encoding Design for Fiber Optic Measurement While Drilling Communication System Based on Superposition Spinal Codes

Low Complexity Encoding Design for Fiber Optic Measurement While Drilling Communication System Based on Superposition Spinal Codes

Super-tokens Auto-encoders for image compression and reconstruction in IoT applications

New telecommunications networks are enabling powerful AI applications for smart cities and transport. These applications require real-time processing of large amounts of media data. Sending data to the cloud for processing is very difficult due to latency and energy constraints. Lossy compression can help, but traditional codecs may not provide enough quality or be efficient enough for resource-constrained devices. This paper proposes a new image compression and processing approach based on variational auto-encoders (VAEs). This VAE-based method aims to efficiently compress images while still allowing for high-quality reconstruction and object detection tasks. The encoder is designed to be lightweight and suitable for devices with limited computing power. The decoder is more complex and uses multi-level vector quantization to reconstruct high-resolution images. This approach allows for a simple encoder on edge devices and a powerful decoder on cloud servers. Key contributions include a low-complexity encoder, a new VAE model based on vector quantization, and a framework for using VAEs in IoT. The first experiments on reconstructed images on CelebA and ImageNet100 datasets show promising results in terms of MS-SSIM, PSNR, MSE and rFID compared to the literature and the ability of our approach to be used in IoT applications. Our approach presents results similar to complex algorithms like compression algorithms BPG in term of trade-off rate-distortion, and hierarchical auto-encoder (HQA) in terms of image reconstruction quality

Performance analysis of Polar Codes in a Visible Light Communication System

One of the vital entity of any communication systems is Channel coding, which leads to the design of high performance codes for future wireless systems having low complexity encoder and decoder design. These systems will have a requirement of operating in highly reliable conditions, maximum throughput, and low and high code rates and to work with short and long information messages. Polar codes are one of the promising error correcting channel codes that can be used in these situations to obtain maximum throughput and coding gain in a communication systems because of their capacity approaching performance and finds interests in Satellite communication and 4G/5G services. These codes uses the concept of channel polarization to be constructed. The work proposed in this paper focusses on the Bit Error Rate evaluation and analysis of the Polar codes using traditional approach and Deep learning approach. The feedforward deep learning networks using different activation functions were used for the Deep learning approach. The Successive Cancellation algorithm and its variant using List decoding were used as traditional decoding methodology. The results obtained using Deep learning approach were satisfactory and was matching as per the traditional decoding.

Polar Coded Repetition

Constructing efficient low-rate error-correcting codes with low-complexity encoding and decoding has become increasingly important for applications involving ultra-low-power devices such as Internet-of-Things (IoT). To this end, schemes based on concatenating the state-of-the-art codes at moderate rates with repetition codes have emerged as practical solutions deployed in various standards. In this paper, we propose a novel mechanism for concatenating outer polar codes with inner repetition codes which we refer to as polar coded repetition. More specifically, we propose to transmit a slightly modified polar codeword by deviating from Arıkan’s standard <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> Kernel in a certain number of polarization recursions at each repetition block. We show how this modification can improve the asymptotic achievable rate of the standard polar-repetition scheme, while ensuring that the overall encoding and decoding complexity is kept almost the same. The achievable rate is analyzed for the binary erasure channel (BEC) and additive white Gaussian noise (AWGN) channel. Moreover, we show that the finite-length performance of the polar coded repetition scheme under cyclic redundancy check (CRC) aided successive cancellation list (SCL) decoder over AWGN channel is better than the uncoded polar-repetition scheme at the cost of a slight increase in decoding complexity. We also compare the proposed scheme, in terms of performance and complexity, with other low-rate solution based on polar codes in the literature.

Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video

Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity.

Construction D’ Lattices for Power-Constrained Communications

Designs and methods for nested lattice codes using Construction D&#x2019; lattices for coding and convolutional code lattices for shaping are described. Two encoding methods and a decoding algorithm for Construction D&#x2019; coding lattices that can be used with shaping lattices for power-constrained channels are given. We construct nested lattice codes with good coding properties, high shaping gain, and low-complexity encoding and decoding. Convolutional code generator polynomials for Construction A lattices with the greatest shaping gain are given, as a result of an extensive search. It is shown that rate 1/3 convolutional codes provide a more favorable performance-complexity trade-off than rate 1/2 convolutional codes. Tail-biting convolutional codes have higher shaping gain than that of zero-tailed convolutional codes. A design for quasi-cyclic low-density parity-check (QC-LDPC) codes to form Construction D&#x2019; lattices which have efficient encoding and indexing is presented. The resulting QC-LDPC Construction D&#x2019; lattices are evaluated using four shaping lattices: the <inline-formula> <tex-math notation="LaTeX">$E_{8}$ </tex-math></inline-formula> lattice, the <inline-formula> <tex-math notation="LaTeX">$BW_{16}$ </tex-math></inline-formula> lattice, the Leech lattice and our best-found convolutional code lattice, showing a shaping gain of approximately 0.65 dB, 0.86 dB, 1.03 dB and 1.25 dB at dimension 2304.

Low Complexity Encoder with Multilabel Classification and Image Captioning燤odel

Due to the advanced development in the multimedia-on-demand traffic in different forms of audio, video, and images, has extremely moved on the vision of the Internet of Things (IoT) from scalar to Internet of Multimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT, which are commonly employed in diverse application areas, especially for capturing remote sensing (RS) images. At the same time, the interpretation of the captured RS image also plays a crucial issue, which can be addressed by the multi-label classification and Computational Linguistics based image captioning techniques. To achieve this, this paper presents an efficient low complexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves the low complexity encoder using the Neighborhood Correlation Sequence (NCS) with a burrows wheeler transform (BWT) technique called LCE-BWT for encoding the RS images captured by the UAV. The application of NCS greatly reduces the computation complexity and requires fewer resources for image transmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presented to determine the multiple class labels of the RS image, shows the novelty of the work. Finally, the Computational Linguistics based Bidirectional Encoder Representations from Transformers (BERT) technique is applied for image captioning, to provide a proficient textual description of the RS image. The performance of the presented technique is tested using the UCM dataset. The simulation outcome implied that the presented model has obtained effective compression performance, reconstructed image quality, classification results, and image captioning outcome.

Universal Covertness for Discrete Memoryless Sources

Consider a sequence X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> of length n emitted by a Discrete Memoryless Source (DMS) with unknown distribution p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> . The objective is to construct a lossless source code that maps X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> to a sequence ∧Y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> of length m that is indistinguishable, in terms of Kullback-Leibler divergence, from a sequence emitted by another DMS with known distribution p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</sub> . The main result is the existence of a coding scheme that performs this task with an optimal ratio m/n equal to H(X)/H(Y), the ratio of the Shannon entropies of the two distributions, as n goes to infinity. The coding scheme overcomes the challenges created by the lack of knowledge about p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> by a type-based universal lossless source coding scheme that produces as output an almost uniformly distributed sequence, followed by another type-based coding scheme that jointly performs source resolvability and universal lossless source coding. The result recovers and extends previous results that either assume p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> or p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</sub> uniform, or p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> known. The price paid for these generalizations is the use of common randomness with vanishing rate, whose length scales as the logarithm of n. By allowing common randomness larger than the logarithm of n but still negligible compared to n, a constructive low-complexity encoding and decoding counterpart to the main result is also provided for binary sources by means of polar codes.

Low-complexity encoder implementation for LDPC codes in CCSDS standard

Low-complexity encoder implementation for LDPC codes in CCSDS standard

A distributed source autoencoder of local visual descriptors for 3D reconstruction

• Application of a deep learning to the compression of local visual descriptors. • Combination of distributed source coding and autoencoders. • Definition of a low-complexity encoder suitable for mobile low-cost boards. • Competitive compression and reconstruction performances w.r.t existing solutions. • Optimization of the coding scheme for 3D scene reconstruction applications. This paper presents a local descriptor coding scheme for multicamera surveillance and 3D reconstruction embedding an autoencoder into a traditional distributed source coding strategy. The proposed solution permits shifting most of the computational complexity at the decoder/receiver and exploiting the correlation among descriptors of different cameras (thus reducing the coded bit rate) without increasing the inter-device communication load. Experimental results show that the proposed scheme permits obtaining a satisfying accuracy with respect to the most recent solutions while generating a limited bit rate.

Edge coloring of graphs with applications in coding theory

In this paper, a new type of edge coloring of graphs together with an algorithm for such an edge coloring is presented to construct some column-weight three low-density parity-check (LDPC) codes whose Tanner graphs are free of 4-cycles. This kind of edge coloring is applied on some well-known classes of graphs such as complete graphs and complete bipartite graphs to generate some column-weight 3 LDPC codes having flexibility in terms of code length and rate. Interestingly, the constructed (3, k)-regular codes with regularities k = 4, 5,…, 22 have lengths n = 12, 20, 26, 35, 48, 57, 70, 88, 104, 117, 140, 155, 176, 204, 228, 247, 280, 301, 330, having minimum block length compared to the best known similar codes in the literature. In addition to linear complexity of generating such parity-check matrices, they can be considered as the base matrices of some quasi-cyclic (QC) LDPC codes with maximum achievable girth 18, which inherit the low-complexity encoder implementations of QC-LDPC codes. Simulation results show that the QC-LDPC codes with large girth lifted from the constructed base matrices have good performances and outperform random codes, progressive edge growth LDPC codes, some finite fields and group rings based QC-LDPC codes and also have a close competition to the standard IEEE 802. 16e (WiMAX) code.

A simple encoder scheme for distributed residual video coding

Rate-Distortion (RD) performance of Distributed Video Coding (DVC) is considerably less than that of conventional predictive video coding. In order to reduce the performance gap, many methods and techniques have been proposed to improve the coding efficiency of DVC with increased system complexity, especially techniques employed at the encoder such as encoder mode decisions, optimal quantization, hash methods etc., no doubt increase the complexity of the encoder. However, low complexity encoder is a widely desired feature of DVC. In order to improve the coding efficiency while maintaining low complexity encoder, this paper focuses on Distributed Residual Video Coding (DRVC) architecture and proposes a simple encoder scheme. The main contributions of this paper are as follows: 1) propose a bit plane block based method combined with bit plane re-arrangement to improve the dependency between source and Side Information (SI), and meanwhile, to reduce the amount of data to be channel encoded 2) present a simple iterative dead-zone quantizer with 3 levels in order to adjust quantization from coarse to fine. The simulation results show that the proposed scheme outperforms DISCOVER scheme for low to medium motion video sequences in terms of RD performance, and maintains a low complexity encoder at the same time.

The Construction of Permutation Group Codes for Communication Systems: Prime or Prime Power?

This paper focuses on correcting a theorem relating to the construction of permutation group codes (PGCs). The theorem in question assumed that affine transformation could be used to enumerate all the code words of a permutation array with a minimum Hamming distance (MHD) of n - 1 for any n > 1. This assumption was founded upon the proposition that, if the code length, n, is a prime power, then the maximum cardinality of the code will be n(n - 1) and its MHD will be n - 1. However, two typical algebraic methods, one relying on affine transformation and another upon the composite operation of two small subgroups of a symmetric group, can violate this proposition. This is because it is only when n is a prime rather than a prime power that they can enumerate all the code words of an (n; n (n - 1) ; n - 1)-PGC. By investigating how the range of n impacts upon the cardinality and MHD of a code, we provide a corrective theorem that stipulates the construction of (p q ; p 2q (1 - 1/p); p q (1 - 1/p))-PGCs when n = pq is a prime power, wherep is a prime and q > 1. On the basis of this theorem, and under the condition of n being the power of 2, we construct a (2 q ; 2 2q-1 ; 2 q-1 )-PGC and present an encoder that can map a k-bit binary information sequence to an n = 2 2q -dimension permutation code word. The natural array structure of (2 q ; 2 2q-1 ; 2 q-1 )-PGCs makes them especially well-suited to forming the basis of low-complexity encoders. We present simulation-based experiments that show that, as the code length increases, the performance of these codes improves. The best performance is for a (16; 128; 8)-PGC, which can achieve -3.8 dB with a word error rate of 10 -7 .

Low complexity high efficiency coding of light fields using ensemble classifiers

Light field images can be efficiently compressed using standard video codecs, such as the High Efficiency Video Coding (HEVC). However, the huge amount of data combined with the high computational complexity of HEVC, poses limitations on high-speed light field capturing and storage. This paper presents a contribution for low complexity encoding of light fields, in different formats using HEVC, based on a Random Forests ensemble classifier. Optimal features for training the classifier are found through a score fusion based approach. Using the HEVC still image profile, the proposed method gives speed-up of 56.23% for sub-aperture images. For pseudo video format, the proposed method outperforms others available in the literature, yielding an average speed-up of 62.18%, 56.54% and 44.73% for Random Access, Low-delay Main and All-Intra profiles respectively, with negligible decrease in RD performance. These are novel results in fast coding of light fields, which are useful for further research and benchmarking.

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.

Efficient QC-LDPC Encoder for 5G New Radio

This paper presents a novel efficient encoding method and a high-throughput low-complexity encoder architecture for quasi-cyclic low-density parity-check (QC-LDPC) codes for the 5th-generation (5G) New Radio (NR) standard. By storing the quantized value of the permutation information for each submatrix instead of the whole parity check matrix, the required memory storage size is considerably reduced. In addition, sharing techniques are employed to reduce the hardware complexity. The encoding complexity of the proposed method was analyzed, and indicated a substantial reduction in the required area as well as memory storage when compared with existing state-of-the-art encoding approaches. The proposed method requires only 61% gate area, and 11% ROM storage when compared with a similar LDPC encoder using the Richardson–Urbanke method. Synthesis results on TSMC 65-nm complementary metal-oxide semiconductor (CMOS) technology with different submatrix sizes were carried out, which confirmed that the design methodology is flexible and can be adapted for multiple submatrix sizes. For all the considered submatrix sizes, the throughput ranged from 22.1–202.4 Gbps, which sufficiently meets the throughput requirement for the 5G NR standard.

On the Error Statistics of Turbo Decoding for Hybrid Concatenated Codes Design

In this paper we propose a model for the generation of error patterns at the output of a turbo decoder. One of the advantages of this model is that it can be used to generate the error sequence with little effort. Thus, it provides a basis for designing hybrid concatenated codes (HCCs) employing the turbo code as inner code. These coding schemes combine the features of parallel and serially concatenated codes and thus offer more freedom in code design. It has been demonstrated, in fact, that HCCs can perform closer to capacity than serially concatenated codes while still maintaining a minimum distance that grows linearly with block length. In particular, small memory-one component encoders are sufficient to yield asymptotically good code ensembles for such schemes. The resulting codes provide low complexity encoding and decoding and, in many cases, can be decoded using relatively few iterations.

A Modified-SCD for Optimize Performance Rate at Polar Code

The first verified capacity-achieving codes were developed by Arikan called “polar codes.” It has gained significant attention due to their low encoding and decoding process complexity and optimum capacity-achievement process. In the original form of polar codes, the decoder has the inferior performance with finite ‘block-lengths' as compared to the LDPC codes, therefore, the successive cancellation decoder (“SCD”) for the polar codes is a very important element in order to provide a capacity theorem, but it comprises complexity at a larger number of iterations that is not applicable for the point of view of lower latency application. Therefore, in this article we proposed a modified SCD that rearranges the stages but it can cause decoder performance degradation, so we applied a novel design rule of the polar codes in order to match with our MSCD model. In the result analysis section, we show the effectiveness of our proposed model that tends to have higher performance rate as compared to standard SCD.

Polar Codes for Informed Receivers

The problem of sending independent messages to a receiver which already has been informed a subset of messages as side information is studied. Which messages are available at the receiver is assumed to be oblivious to the transmitter. A simple yet powerful construction of codes for informed receivers based on polar codes is proposed to efficiently and fairly convert side information into reduction in probability of errors for any message side information. Compared with existing designs based on codes on graph or algebraic codes, the proposed construction is both conceptually and practically simple. Moreover, the codes inherit many good properties of polar codes, including explicit construction and low-complexity encoding and decoding scaling such as $\mathcal {O}(N\log N)$ where $N$ is the blocklength. Extensions to high-order modulation and block fading channels are also proposed. Simulation results indicate that for every side information configuration, the proposed codes can convert side information into reduction in signal-to-noise ratio of roughly 6 dB/bit at bit error rate of 10−5 under additive white Gaussian noise channel or block fading channel.