Entropy Coding Research Articles

An Iterative Lifting Scheme on DCT Coefficients for Image Coding

Image coding is considered to be more effective, as it reduces number of bits required to store and/or to transmit image data. Transform based image coders play a significant role as they decorrelate the spatial low level information. It is found utilization in International compression standards such as JPEG, JPEG 2000, MPEG and H264. The choice of transform is an important issue in all these transforms coding schemes. Most of the literature suggests either Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT). In this proposed work, the energy preservation of DCT coefficients is analysed, and to down sample these coefficients, lifting scheme is iteratively applied so as to compensate the artifacts that appear in the reconstructed picture, and to yield the higher compression ratio. This is followed by scalar quantization and entropy coding, as in JPEG. The performance of the proposed iterative lifting scheme, employed on decorrelated DCT coefficients is measured with standard Peak Signal to Noise Ratio (PSNR) and the results are encouraging.

Onboard Image Processing System for Hyperspectral Sensor.

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

Sequential block-based disparity map estimation algorithm for stereoscopic image coding

This paper deals with the problem of block-based disparity map estimation for stereoscopic image coding where the estimated map is transmitted to the decoder in order to predict one view from the other. The estimation problem of the disparity map is thus a trade-off between the quality prediction and the binary cost of the disparities to be stored or transmitted. This trade-off is modeled as a joint entropy-distortion metric assuming that the disparity map is encoded with an entropy coder; and one of the two views is fully predicted using this map when applied to the other view. However minimizing this joint metric is a complex combinatorial optimization problem where choices of disparities are all interrelated. A sub-optimal optimization solution is then proposed. It is based on a tree structure which is constructed sequentially whenever a block is matched. The developed algorithm, called Modified M-Algorithm (MMA), processes the reference view in a raster scanning order and assumes that the disparities to be selected in the unprocessed area are likely to follow a chosen disparity distribution. This algorithm has the ability at each step of the process not only to retain the M-best paths of the tree in terms of entropy-distortion cost but also to explore all possible extensions of each of these M paths until reading the last block of the view. Simulations, conducted on stereoscopic images extracted from Middlebury and Deimos datasets, show the advantage of our MMA compared to the conventional Block Matching Algorithm (BMA) with and without regularization both in terms of reducing bitrate and distortion.

Model for Estimation of Bounds in Digital Coding of Seabed Images

This paper proposes the novel model for estima- tion of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density func- tion of the quantizer input signal, SQNR gains are calcu- lated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution.

Fast Mode Selection for HEVC Intra-Frame Coding With Entropy Coding Refinement Based on a Transparent Composite Model

In comparison with H.264/Advanced Video Coding, the newest video coding standard, High Efficiency Video Coding (HEVC), improves video coding rate-distortion (RD) performance, but at the price of significant increase in its encoding complexity, especially, in intra-mode decision due to the adoption of more complex block partitions and more candidate intra-prediction modes (IPMs). To reduce the mode decision complexity in HEVC intra-frame coding, while maintaining its RD performance, in this paper, we first formulate the mode decision problem in intra-frame coding as a Bayesian decision problem based on the newly proposed transparent composite model (TCM) for discrete cosine transform coefficients, and then present an outlier-based fast intra-mode decision (OIMD) algorithm. The proposed OIMD algorithm reduces the complexity using outliers identified by TCM to make a fast coding unit split/nonsplit decision and reduce the number of IPMs to be compared. To further take advantage of the outlier information furnished by TCM, we also refine entropy coding in HEVC by encoding the outlier information first, and then the actual mode decision conditionally given the outlier information. The proposed OIMD algorithm can work with and without the proposed entropy coding refinement. Experiments show that for the all-intra-main test configuration of HEVC: 1) when applied alone, the proposed OIMD algorithm reduces, on average, the encoding time (ET) by 50% with 0.7% Bjontegaard distortion (BD)-rate increase and 2) when applied in conjunction with the proposed entropy coding refinement, it reduces, on average, both the ET by 50% and BD-rate by 0.15%.

Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery

This paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements are encoded into compressed bitstream by entropy coding. Specifically, to reduce the quantization errors of measurements, a nonuniform quantization is integrated into the DPCM-based quantizer. At the decoder side, a novel joint reconstruction algorithm is proposed to improve the quality of reconstructed video frames. Firstly, the proposed algorithm uses the temporal autoregressive (AR) model to generate the Side Information (SI) of video frame, and next it recovers the residual between the original frame and the corresponding SI. To exploit the sparse property of residual with locally varying statistics, the Principle Component Analysis (PCA) is used to learn online the transform matrix adapting to residual structures. Extensive experiments validate that the joint reconstruction algorithm in the proposed codec achieves much better results than many existing methods with consideration of the reconstructed quality and the computational complexity. The rate-distortion performance of the proposed codec is superior to the state-of-the-art CS-based video codec, although there is still a considerable gap between it and traditional video codec.

An Efficient Adaptive Binary Range Coder and Its VLSI Architecture

In this paper, we propose a new hardware-efficient adaptive binary range coder (ABRC) and its very-large-scale integration (VLSI) architecture. To achieve this, we follow an approach that allows to reduce the bit capacity of the multiplication needed in the interval division part and shows how to avoid the need to use a loop in the renormalization part of ABRC. The probability estimation in the proposed ABRC is based on a lookup table free virtual sliding window. To obtain a higher compression performance, we propose a new adaptive window size selection algorithm. In comparison with an ABRC with a single window, the proposed system provides a faster probability adaptation at the initial encoding/decoding stage, and more accurate probability estimation for very low entropy binary sources. We show that the VLSI architecture of the proposed ABRC attains a throughput of 105.92 MSymbols/s on the FPGA platform, and consumes 18.15 mW for the dynamic part power. In comparison with the state-of-the-art MQ-coder (used in JPEG2000 standard) and the M-coder (used in H.264/Advanced Video Coding and H.265/High Efficiency Video Coding standards), the proposed ABRC architecture provides comparable throughput, reduced memory, and power consumption. Experimental results obtained for a wavelet video codec with JPEG2000-like bit-plane entropy coder show that the proposed ABRC allows to reduce the bit rate by 0.8%–8% in comparison with the MQ-coder and from 1.0%–24.2% in comparison with the M-coder.

Lossless intra coding on whole frame for screen content video based on template matching

The-state-of-art video compression standards,such as H. 264 and high efficiency video coding( HEVC) are mainly designed towards the camera-captured video. However,screen content( SC) video is created by the computer without noise,which needs higher efficiency than the camera-captured video. SC picture contains more structural similarity in contrast to camera-captured picture which could be used to improve the intra-picture compression ratio. A template matching method( TMM) was proposed by exploiting this similarity for SC intra lossless picture. The 21 pixel points template and the high efficiency 24-bits hash table were designed in TMM intra frame coding. For every picture,the first step was to use template matching for every pixel,and then speed up the matching processing by hash function,finally encode the residual picture by entropy coding. Experimental results show that TMM can double the lossless compression ratio compared to HEVC range extensions version 4. 1 and reduce the compression time by half.

A Novel Computational Model for TransformCoding of Colour Images with OrthogonalPolynomials in difference Colour Spaces

In this paper, a new computational model for transform coding of colour images based on a set of orthogonal polynomials is proposed in different colour spaces. Initially, a three dimensional point spread operator is devised from a set of orthogonal polynomials and a linear transformation is defined in terms of matrix operation that takes less computation time. The proposed model is configured to take not only the interactions in the individual colour planes but also takes into account interactions between the colour planes. Based on the energy grouping property of orthogonal polynomials transform, the single dimensional energy vectors generated by point spread operator of proposed transform coding are rearranged so that the high energy transformed coefficients are grouped to yield higher compression ratio. After the proposed transformation, the transform coefficients of the colour images are subjected to quantization and entropy coding with a new nipple-dictionary substitution technique. The performance of the proposed colour image transform coding is reported by computing the peak-signal-to-noise ratio and is compared with DCT and wavelet based coding schemes. The proposed model is experimented in RGB colour space and extended to different colour spaces such as YCbCr, YIQ, XYZ and HSI models

Does an Arithmetic Coding Followed by Run-length Coding Enhance the Compression Ratio?

Compression is a technique to minimize the quantity of image without excessively decreasing the quality of the image. Then, the translating of compressed image is much more efficient and rapidly than original image. Arithmetic and Huffman coding are mostly used techniques in the entropy coding. This study tries to prove that RLC may be added after Arithmetic coding as an extra processing step which may therefore be coded efficiently without any further degradation of the image quality. So, the main purpose of this study is to answer the following question

Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian–Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of this paper is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from colocated sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared with the state-of-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system.

Human Motion Capture Data Tailored Transform Coding

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed.

Image Compression Based on Discrete Cosine Transform and Multistage Vector Quantization

In this paper, an image compression scheme is proposed, based on discrete cosine transform (DCT). This scheme is a hybrid method, which combines vector quantization (VQ) and differential pulse code modulation (DPCM). This scheme begins with transforming image from spatial domain to frequency domain using DCT. Then the block data is transformed into a vector according to zigzag order, and then truncated. After that, the vector is split into DC coefficient and AC coefficients. After scale quantization, DC coefficient is coded using DPCM. AC coefficients are coded using multistage vector quantization (MSVQ). Then, entropy encoding is performed on index-tables and DC part, separately. The experimental results show that, compared to conventional VQ and DCTVQ schemes, proposed scheme has a better performance.

Context adaptive binary arithmetic coding-based data hiding in partially encrypted H.264/AVC videos

A scheme of data hiding directly in a partially encrypted version of H.264/AVC videos is proposed which includes three parts, i.e., selective encryption, data embedding and data extraction. Selective encryption is performed on context adaptive binary arithmetic coding (CABAC) bin-strings via stream ciphers. By careful selection of CABAC entropy coder syntax elements for selective encryption, the encrypted bitstream is format-compliant and has exactly the same bit rate. Then a data-hider embeds the additional data into partially encrypted H.264/AVC videos using a CABAC bin-string substitution technique without accessing the plaintext of the video content. Since bin-string substitution is carried out on those residual coefficients with approximately the same magnitude, the quality of the decrypted video is satisfactory. Video file size is strictly preserved even after data embedding. In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain. Experimental results have demonstrated the feasibility and efficiency of the proposed scheme.

Image data compression with hierarchical pixel averaging and fully adaptive prediction error coder

The fully adaptive prediction error coder (FAPEC) is an entropy coder that typically offers better results than the adaptive Rice compressor. It uses basic preprocessing stages such as delta preprocessing, but it can also be combined with a discrete wavelet transform. We describe a new algorithm called hierarchical pixel averaging (HPA). It divides an image into blocks of 16 x 16 pixels, which are subsequently divided into smaller blocks, up to the basic level where one block corresponds to one pixel. Average pixel values are determined for each level from which differential coefficients are extracted. HPA allows the introduction of controlled losses with several quality levels, also allowing to progressively decompress a given image from lower to higher quality. It achieves better resolution in sharp image edges when compared to other lossy algorithms. HPA is based on simple arithmetic operations, allowing a very simple (thus quick) implementation. It does not use any floating-point operations, which is an interesting feature for satellite or embedded data compression. We present a first implementation of HPA and the results obtained on a variety of images, both for the lossless and lossy cases with different quality levels. Our results indicate that HPA + FAPEC offer a performance comparable to that of CCSDS 122.0. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Entropy Coder for Audio Signals

Abstract In the paper an effective entropy coder designed for coding of prediction errors of audio signals is presented. The coder is implemented inside a greater structure, which signal modeling part is a lossless coding backward adaptation algorithm consisting of cascaded Ordinary Least Squares (OLS), three Normalized Least Mean Square (NLMS), and prediction error bias correction sections. The technique performance is compared to that of four other lossless codecs, including MPEG-4 Audio Lossless (ALS) one, and it is shown that indeed, on the average the new method is the best. The entropy coder is an advanced context adaptive Golomb one followed by two context adaptive arithmetic coders.

A Comparative Study of Video Compression Techniques

In modern world more image and video compression technologies improving gradually. But major barrier of the new technologies concepts are repeated from authors. Because authors can’t find in depth of the papers from various technologies. So Survey of Video Compression Techniques very helpful for these types problems in video compression areas. Video compression techniques such as DCT coding, Quantization, Entropy coding, Motion estimation are widely used in video compression techniques. The main focus of this paper is to analyse video compression techniques required for video processing especially to discover how much amount of data to compressed, which techniques is faster and visual quality better and so on. We evaluate the video compression techniques for finding compression ratio in terms of performance, speed and accuracy. We also compare video compression techniques with conventional methods.

The Non-Regular CEO Problem

We consider the CEO problem for non-regular source distributions (such as uniform or truncated Gaussian). A group of agents observe independently corrupted versions of data and transmit coded versions over rate-limited links to a CEO. The CEO then estimates the underlying data based on the received coded observations. Agents are not allowed to convene before transmitting their observations. This formulation is motivated by the practical problem of a firm’s CEO estimating (non-regular) beliefs about a sequence of events, before acting on them. Agents’ observations are modeled as jointly distributed with the underlying data through a given conditional probability density function. We study the asymptotic behavior of the minimum achievable mean squared error distortion at the CEO in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. We establish a $1/R^{2}$ convergence of the distortion, an intermediate regime of performance between the exponential behavior in discrete CEO problems [Berger, Zhang, and Viswanathan (1996)], and the $1/R$ behavior in Gaussian CEO problems [Viswanathan and Berger (1997)]. Achievability is proved by a layered architecture with scalar quantization, distributed entropy coding, and midrange estimation. The converse is proved using the Bayesian Chazan–Zakai–Ziv bound.

Crypto-Compression System: An Integrated Approach using Stream Cipher Cryptography and Entropy Encoding

s faced in modern communications are not only just related to security but also concerned with the communication speed and content size.Now day's networks demand exchange of information with more security and reduction in both -data storage and the time for data transmission. This can be realized by adopting an integrated approach usingcompression and encryption techniques, such a system is termed as crypto- compression system. Encryption is a coding technique that provides security whereas data compression is also a coding technique, whose purpose is to reduce both the data storage size and ultimately the time for data transmission. In this paper, an algorithm has been proposed which uses the compression and data encryption techniques. Firstly, data size is reduced through various compression techniques in order to increase the data transfer rate. Then the compressed data is encrypted to raise its security. Thus, technique proposed in this paper is useful in reducing data size, raising data transfer rate and providing security during communication. In this proposed system, encoded string is created from an input string of symbols and characters based on entropy encoding technique like arithmetic coding that can be used to achieve high level of compression in the present network topologies for exchange of data with more security and compression. Keywordscoding, one-time pads, stream cipher cryptography, entropy encoding, crypto-compression system

Fuzzy concept lattice reduction using Shannon entropy and Huffman coding

In the last decade, formal concept analysis (FCA) in a fuzzy setting has received more attention for knowledge processing tasks in various fields. The hierarchical order visualisation of generated formal concepts is a major concern for the practical application of FCA. In this process, a major issue is the huge number of formal concepts generated from ‘a large context’, and another problem is their ‘storage’ complexity. To deal with these issues a method is proposed in this paper based on Shannon entropy and Huffman coding. The proposed method is illustrated using crisply generated concepts such that the changes between obtained concepts can be measured using Levenshtein distance. The analysis derived from the proposed method is illustrated with an example for FCA in a fuzzy setting.