Entropy Coding Research Articles

Distributed Distortion-Rate Optimized Compressed Sensing in Wireless Sensor Networks

This paper addresses lossy distributed source coding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by each sensor, followed by the joint reconstruction of the sources at the decoder. We develop a novel complexity-constrained distributed variable-rate quantized CS method, which minimizes a weighted sum between the mean square error signal reconstruction distortion and the average encoding rate. The encoding complexity of each sensor is restrained by pre-quantizing the encoder input, i.e., the CS measurements, via vector quantization. Following the entropy-constrained design, each encoder is modeled as a quantizer followed by a lossless entropy encoder, and variable-rate coding is incorporated via rate measures of an entropy bound. For a two-sensor system, necessary optimality conditions are derived, practical training algorithms are proposed, and complexity analysis is provided. Numerical results show that the proposed method achieves superior compression performance as compared with baseline methods, and lends itself to versatile setups with different performance requirements.

Study and analysis of wavelet based image compression techniques

This paper presented comprehensive study with performance analysis of very recent Wavelet transform based image compression techniques. Image compression is one of the necessities for such communication. The goals of image compression are to minimize the storage requirement and communication bandwidth. Compression is achieved by the removal of redundant data. Discrete Wavelet Transform (DWT) is a recently developed compression technique in image compression. DWT image compression includes decomposition (transform of image), Detail coefficients thresholding, and entropy encoding. This paper mainly describes the transform of an image using DWT and thresholding techniques. In this paper we have taken the standard image Lena of size 256X256 of 8 bit depth and applied DWT (haar). Then two results set are obtained by applying two different techniques of thresholding and then compare the result.Keywords: Discrete Cosine Transform, Wavelet Transform, JPEG Compression and Entropy Encoding

Optimized Context Weighting for the Compression of the Un-repetitive Genome Sequence Fragment

The details of context weighting is discussed first and some fomulas for describing the progress of context weighting is also given. During the weighting, the weighting cost can influence the performance of context modeling entropy coding. However, the context weighting rely on corresponding counting vectors which are joined in weighting. Meanwhile, the equivalence between context weighting and the weighting of description length implies that the weights can be optimized. The corresponding methods are discussed in details in our paper. At last, the proposed approach is used to compress these simple genome sequences. A large number of experiment results indicate that the corresponding compression results is better than the results of other existing algorithms.

Weather Radar Data Compression Based on Spatial and Temporal Prediction

The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By upgrading the capabilities of radar, the amount of data that can be processed continues to increase. Weather radar compression is necessary to reduce the amount of data for transmission and archiving. The characteristics of weather radar data are not considered in general-purpose compression programs. The sparsity and data redundancy of weather radar data are analyzed. A lossless compression of weather radar data based on prediction coding is presented, which is called spatial and temporal prediction compression (STPC). The spatial and temporal correlations in weather radar data are utilized to improve the compression ratio. A specific prediction scheme for weather radar data is given, while the residual data and motion vectors are used to replace the original values for entropy coding. After this, the Level-II product from CINRAD SA is used to evaluate STPC. Experimental results show that the STPC achieves a better performance than the general-purpose compression programs, with the STPC yield being approximately 26% better than the next best approach.

Joint Image Watermarking, Compression and Encryption Based on Compressed Sensing and Entropy Coding

Joint Image Watermarking, Compression and Encryption Based on Compressed Sensing and Entropy Coding

Entropy Coding Aided Adaptive Subcarrier-Index Modulated OFDM

We propose entropy coding-aided adaptive subcarrier-index-modulated orthogonal frequency division multiplexing (SIM-OFDM). In conventional SIM-OFDM, the indices of the subcarriers activated are capable of conveying extra information. We propose the novel concept of compressing the index information bits by employing the Huffman coding. The probabilities of the different subcarrier activation patterns are obtained from an optimization procedure, which improves the performance of the scheme. Both the maximum-likelihood as well as the logarithmic-likelihood ratio-based soft detector may be employed for detecting the subcarriers activated as well as the information mapped to the classic constellation symbols. As an additional advantage of employing the variable-length Huffman codebook, all the legitimate subcarrier activation patterns may be employed, whereas the conventional SIM-OFDM is capable of using only a subset of the patterns. Our simulation results show that an improved performance is attainable by the proposed system.

Lossless Compression of Neural Signals with Predictor Schemes Achieving more than fivefold data Reduction of in-vitro Recorded Retinal Signals

Lossless Compression of Neural Signals with Predictor Schemes Achieving more than fivefold data Reduction of in-vitro Recorded Retinal Signals

Enhanced Image Coding Scheme Based on Modified Embedded Zerotree Wavelet Transform (DMEZW)

In this paper the proposed scheme uses different processing methods by applying Integer Lifting Wavelet Transform (ILWT) on gray scale image generating four subband is presented. The low frequency subbands is compressed losslessly by the Developed Modified Embedded Zerotree Wavelet Transform (DMEZW) directly. The high and middle frequency subbands are compressed lossyly by applying first to single stage Vector Quantization (VQ) then to DMEZW, finally generating two vectors ready for entropy coding and it is presented as Arithmetic Coding (AC) to produce a bit stream to be stored or transmitted. The main improvements of DMEZW is done by modifying the scanning strategy of the wavelet coefficients and the quantization threshold. The high and low frequency subbands are manipulated separately. The experimental results show that the developed method can improve the quality of the recovered image and the encoding efficiency. The proposed scheme programming code has achieved high Compression Ratio (CR) and remarkable Peak Signal to Noise Ratio (PSNR).

High efficient video coding using weighted entropy with optimized quantization matrix

As the concept of quantization matrix becomes an important feature in recent video CODECs, an optimized quantization matrix is being considered in the High-Efficiency Video Coding (HEVC) standard. This paper describes the entropy encoding by familiarizing optimized quantization matrix, and so higher rate of compression can be accomplished over the improved entropy encoding. Experiments show that for the eight benchmark video sequences and PSNR for varying rate of data transmission is explored. Comparative analysis is made with the improved (WE-Encoding) and standard entropy encoding based on the performance measurements. The simulation results show that the proposed method (WE-OQM) can save the originality of the decoded video sequence far better even though the compression rate is increased. In addition, the overall analysis states that the proposed method is 35.29% better than the Standard Encoding and 62.5% better than the WE-Encoding.

FAPEC, a versatile and efficient data compressor for space missions

ABSTRACTFully Adaptive Prediction Error Coder (FAPEC) is a data compressor which offers good compression ratios even when data is severely affected by noise and outliers. This, combined with its high computing performance, makes it suitable for space applications. It is based on a variety of pre-processing stages followed by an outlier-resilient adaptive entropy coder. Sample sizes of 8–24 bits are supported. Pre-processing stages range from simple delta algorithms (with interleaving, lossy, and filtering options) to more complex discrete wavelet transform and Hierarchical Pixel Averaging image algorithms (with or without losses, for single or multiple bands). A quick prediction-based lossless multi-band algorithm is also available, as well as lightweight dictionary-like pattern recognition. Thus, one can have functionalities equivalent to those of Consultative Committee for Space Data Systems (CCSDS) 121.0, 122.x, and 123.x in a single system, as well as some features not supported yet by any recommendation. Here, we describe the main features of FAPEC and the results obtained on a variety of data, comparing compression ratios and speeds against CCSDS standards.

Design of A Multimedia Bitstream ASIP for Multiple CABAC Standards

The complexity of image compression algorithms has increased in order to improve image compression efficiency. One way to resolve high computational complexity is parallel processing. However, entropy coding, which is lossless compression, does not fit into the parallel processing form because of the correlation between consecutive symbols. This paper proposes a new application-specific instruction set processor (ASIP) platform by adding new context-adaptive binary arithmetic coding (CABAC) instructions to the existing platform to quickly process a variety of entropy coding. The newly added instructions work without conflicts with all other existing instructions of the platform, providing the flexibility to handle many coding standards with fast processing speeds. CABAC software is implemented for High Efficiency Video Coding (HEVC) and the performance of the proposed ASIP platform was verified with a field programmable gate array simulation.

Design of a Compressive Sensing Based Fall detection System for Elderly Using WSN

Recent researches have pointed out that one third persons are aged 65 and above requires special health care. As the number of elderly person is increasing, home monitoring for healthcare applications is playing a vital role in our daily life. Falls are one of the unpredicted but hazardous events. A sudden fall has to be informed to the caretaker immediately and wireless sensor networks are capable of sensing these falls. In this framework, fall frames are identified from a monitored video and transmitted using wireless sensor nodes. Since the multimedia data requires high bandwidth, there is a need for efficient compression. In this paper, compressed sensing based fall detection system for elderly using WSN (CSFDS) is proposed. A quantization with entropy coding is incorporated in this new fall detection framework for achievement of the efficient video compression. Performance evaluation is done using parameters like peak signal to noise ratio, structural similarity index, transmission energy, delay and packet loss. Simulation results show the CSFDS framework outperforming the raw frame transmission by achieving 83.4% reduction in transmission energy and time. An average PSNR and SSIM value of 34.67 dB and 0.9706 respectively is achieved.

A Lightweight Contextual Arithmetic Coder for On-Board Remote Sensing Data Compression

The Consultative Committee for Space Data Systems (CCSDS) has issued several data compression standards devised to reduce the amount of data transmitted from satellites to ground stations. This paper introduces a contextual arithmetic encoder for on-board data compression. The proposed arithmetic encoder checks the causal adjacent neighbors, at most, to form the context and uses only bitwise operations to estimate the related probabilities. As a result, the encoder consumes few computational resources, making it suitable for on-board operation. Our coding approach is based on the prediction and mapping stages of CCSDS-123 lossless compression standard, an optional quantizer stage to yield lossless or near-lossless compression and our proposed arithmetic encoder. For both lossless and near-lossless compression, the achieved coding performance is superior to that of CCSDS-123, M-CALIC, and JPEG-LS. Taking into account only the entropy encoders, fixed-length codeword is slightly better than MQ and interleaved entropy coding.

An efficient hardware architecture of CAVLC encoder based on stream processing

The paper presents an efficient implementation of Context-Adaptive Variable Length Coding (CAVLC) entropy encoder in H.264/AVC standard. The architecture is designed with a parallel structure targeting real-time video compression. The intensive memory access demand in the syntax element coding stage is lowered by using the proposed arithmetic table elimination technique. The packing stage implementation is interleaved with syntax element generation stage and includes fast methods for syntax elements concatenation. The register update method performs concatenation of the bitstream of previously processed sub-blocks and the syntax codewords of the currently processed sub-block. The CAVLC encoder processes 4 × 4 sub-block coefficients in parallel, introducing the initial latency of 12 clock cycles, after which the full pipeline of the data encoding on the sub-block level is performed, and 16 residuals are processed at each clock cycle. The achieved high throughput allows the encoding core to perform real-time processing of 8K UHD (4320p) video sequences with a frame rate of 30 frames/s.

A selective encryption scheme of CABAC based on video context in high efficiency video coding

In this paper, we propose a selective encryption scheme with a chaotic encryption system for the High Efficiency Video Coding (HEVC) standard in which Context Adaptive Binary Arithmetic Coding (CABAC) is the only entropy coder for transform coefficient coding. Our method focuses on the binstrings of truncated rice with a context “p” (TRp) code suffix and kth order Exp-Golomb (k = p + 1) code suffix before Binary Arithmetic Coding (BAC) for the remaining absolute level coding, which is coded by the bypass mode in the entropy coding stage. The probability of symbols does not change and CABAC decoding has no effect after encryption. Several different YUV sequences are used for experimental evaluation of the proposed algorithm. Compared to previous researches, our approach has good protection for video information, which keeps a constant bitrate and format compatibility, and meantime it has a negligible impact on encoding performance.

Significance of pre-processing and its impact on lossless hyperspectral image compression

ABSTRACTThe primitive aspect of hyperspectral imagery is its inherent spatial and spectral correlation. This correlation is exploited by subjecting the imaging cube to compression. A new approach to accomplish lossless hyperspectral image compression has been proposed. The imaging cube is subjected to pre-processing stage prior to entropy coding. Pre-processing stage comprises band normalization, ordering of bands followed by image scanning. A new sorting technique entitled Greedy Heap Sorting is suggested. The proposed strategy yields an average compression ratio (CR) of 4.93 and average bits per pixel (bpp) of 3.08. The proficiency of the system is on par with the existing contemporary algorithms for lossless hyperspectral image compression in terms of CR, bpp and reduced complexity.

A Practical Hybrid Digital-Analog Scheme for Wireless Video Transmission

We propose a hybrid digital-analog framework for wireless video transmission, which benefits from both the high distortion-power performance of digital systems and the graceful performance degradation of analog systems. The proposed framework models video frames as a parallel Gaussian source, which is separated into digital and analog parts through scalar quantization. It features entropy coding and channel coding in digital transmission and power scaling in analog transmission. The key challenge in this framework is how to allocate the constrained power and bandwidth resources between and among digital and analog components to achieve minimal distortion at the receiver. Given the worst-case channel signal-to-noise ratio, we are able to derive a closed-form expression of the overall distortion. However, minimizing it is a mixed-integer non-linear programming problem, which is generally non-deterministic polynomial-time hard. By making reasonable and justified simplifications, we approach the optimal solution through a practical scheme. Evaluations show that the proposed scheme outperforms the state-of-the-art analog scheme SoftCast by a large margin. The gain in received video peak signal-to-noise ratio is up to 5.0 dB for various types of videos.

Transform Coding for Point Clouds Using a Gaussian Process Model.

We propose using stationary Gaussian processes (GPs) to model the statistics of the signal on points in a point cloud, which can be considered samples of a GP at the positions of the points. Furthermore, we propose using Gaussian process transforms (GPTs), which are Karhunen-Loève transforms of the GP, as the basis of transform coding of the signal. Focusing on colored 3D point clouds, we propose a transform coder that breaks the point cloud into blocks, transforms the blocks using GPTs, and entropy codes the quantized coefficients. The GPT for each block is derived from both the covariance function of the GP and the locations of the points in the block, which are separately encoded. The covariance function of the GP is parameterized, and its parameters are sent as side information. The quantized coefficients are sorted by the eigenvalues of the GPTs, binned, and encoded using an arithmetic coder with bin-dependent Laplacian models, whose parameters are also sent as side information. Results indicate that transform coding of 3D point cloud colors using the proposed GPT and entropy coding achieves superior compression performance on most of our data sets.

Quadtree coding with adaptive scanning order for space-borne image compression

Space-borne equipments produce very big images while their capacities of storage, calculation and transmission are limited, so low-complexity image compression algorithms are necessary. In this paper, we develop an efficient image compression algorithm based on quadtree in wavelet domain for this mission. First, we propose an adaptive scanning order for quadtree, which traverses prior the neighbors of previous significant nodes from bottom to the top of quadtree, so that more significant coefficients are encoded at a specified bit rate. Second, we divide the entire wavelet image to several blocks and encode them individually. Because the distortion-rate usually decreases as the level of the quadtree increases with the adaptive scanning order, to control bit rate for each block, we set the points exactly after coding each level of the quadtree as the candidate truncation points. The proposed method can provide quality, position and resolution scalability, which is simple and fast without any entropy coding, so it is very suitable for space-borne equipments. Experimental results show that it attains better performance compared with some state-of-the-art algorithms.

A Video Sensing Oriented Format-compliant Entropy Coding Encryption Scheme and Embedded Video Processing System

Information security is an important issue in IOT applications such as multimedia sensing. Firstly, the study is aiming to achieve a balance between the security of massive multimedia data and the limited computation with energy resources in IOT system, while an analysis model for optimizing the protection of multimedia data is proposed. Secondly, a new lightweight format-compliant video encryption scheme is presented. By encrypting the key initial index values of the context model in CABAC entropy coding, the proposed encryption scheme achieves low complexity and could meet the real-time requirement of an unmanned aerial vehicle based video capture system. Thirdly, an embedded secure video processing system using DSP and ARM is designed, and the presented encryption scheme is implemented. Finally, the experimental results show that the encryption scheme can get a tradeoff between the efficiency and security.