Entropy Coding Research Articles

Satellite multispectral image compression based on removing sub-bands

This paper presents an efficient technique for satellite multispectral image compression aiming at reducing the size of storage of multispectral images with high-quality reconstruction. The proposed technique is based on removing sub-bands before compression. The removed sub-bands are determined using the correlation coefficients between bands. In the compression process, we use the Discrete Wavelet Transform (DWT) followed by an entropy coder (e.g., a Huffman or an arithmetic encoder) for the most correlated bands. Moreover, we use JPEG2000 to compress the rest of bands with Principal Component Analysis (PCA) as a spectral decorrelation transform. Enhanced Thematic Mapper plus (ETM+) satellite multispectral images are used for the validation of the proposed technique. Experiments results demonstrate that the proposed technique improves the average multispectral image quality by 3–11dB. The experimental results verify the effectiveness of the proposed technique.

Energy-efficient image compression algorithm for high-frame rate multi-view wireless capsule endoscopy

The design, architecture and VLSI implementation of an image compression algorithm for high-frame rate, multi-view wireless endoscopy is presented. By operating directly on Bayer color filter array image the algorithm achieves both high overall energy efficiency and low implementation cost. It uses two-dimensional discrete cosine transform to decorrelate image values in each $$4\times 4$$ block. Resulting coefficients are encoded by a new low-complexity yet efficient entropy encoder. An adaptive deblocking filter on the decoder side removes blocking effects and tiling artifacts on very flat image, which enhance the final image quality. The proposed compressor, including a 4 KB FIFO, a parallel to serial converter and a forward error correction encoder, is implemented in 180 nm CMOS process. It consumes 1.32 mW at 50 frames per second (fps) and only 0.68 mW at 25 fps at 3 MHz clock. Low silicon area 1.1 mm $$\times$$ 1.1 mm, high energy efficiency (27 $$\upmu$$ J/frame) and throughput offer excellent scalability to handle image processing tasks in new, emerging, multi-view, robotic capsules.

Medical Image Compression using Adaptive Prediction and Block based Entropy Coding

Medical Image Compression using Adaptive Prediction and Block based Entropy Coding

Compressed and raw video steganography techniques: a comprehensive survey and analysis

In the last two decades, the science of covertly concealing and communicating data has acquired tremendous significance due to the technological advancement in communication and digital content. Steganography is the art of concealing secret data in a particular interactive media transporter, e.g., text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques have become important in many video-sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of the noteworthy development of advanced video over the Internet. The performance of any steganographic method ultimately relies on the imperceptibility, hiding capacity, and robustness. In the past decade, many video steganography methods have been proposed; however, the literature lacks of sufficient survey articles that discuss all techniques. This paper presents a comprehensive study and analysis of numerous cutting edge video steganography methods and their performance evaluations from literature. Both compressed and raw video steganography methods are surveyed. In the compressed domain, video steganography techniques are categorized according to the video compression stages as venues for data hiding such as intra frame prediction, inter frame prediction, motion vectors, transformed and quantized coefficients, and entropy coding. On the other hand, raw video steganography methods are classified into spatial and transform domains. This survey suggests current research directions and recommendations to improve on existing video steganography techniques.

A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT.

This paper presents a novel data compression and transmission scheme for power reduction in Internet-of-Things (IoT) enabled wireless sensors. In the proposed scheme, data is compressed with both lossy and lossless techniques, so as to enable hybrid transmission mode, support adaptive data rate selection and save power in wireless transmission. Applying the method to electrocardiogram (ECG), the data is first compressed using a lossy compression technique with a high compression ratio (CR). The residual error between the original data and the decompressed lossy data is preserved using entropy coding, enabling a lossless restoration of the original data when required. Average CR of 2.1 × and 7.8 × were achieved for lossless and lossy compression respectively with MIT/BIH database. The power reduction is demonstrated using a Bluetooth transceiver and is found to be reduced to 18% for lossy and 53% for lossless transmission respectively. Options for hybrid transmission mode, adaptive rate selection and system level power reduction make the proposed scheme attractive for IoT wireless sensors in healthcare applications.

Improved entropy encoding for high efficient video coding standard

The High Efficiency Video Coding (HEVC) has better coding efficiency, but the encoding performance has to be improved to meet the growing multimedia applications. This paper improves the standard entropy encoding by introducing the optimized weighing parameters, so that higher rate of compression can be accomplished over the standard entropy encoding. The optimization is performed using the recently introduced firefly algorithm. The experimentation is carried out using eight benchmark video sequences and the PSNR for varying rate of data transmission is investigated. Comparative analysis based on the performance statistics is made with the standard entropy encoding. From the obtained results, it is clear that the originality of the decoded video sequence is preserved far better than the proposed method, though the compression rate is increased.

Power Distortion Optimization for Uncoded Linear Transformed Transmission of Images and Videos.

Recently, there is a resurgence of interest in uncoded transmission for wireless visual communication. While conventional coded systems suffer from cliff effect as the channel condition varies dynamically, uncoded linear-transformed transmission (ULT) provides elegant quality degradation for wide channel SNR range. ULT skips non-linear operations, such as quantization and entropy coding. Instead, it utilizes linear decorrelation transform and linear scaling power allocation to achieve optimized transmission. This paper presents a theoretical analysis for power-distortion optimization of ULT. In addition to the observation in our previous work that a decorrelation transform can bring significant performance gain, this paper reveals that exploiting the energy diversity in transformed signal is the key to achieve the full potential of decorrelation transform. In particular, we investigated the efficiency of ULT with exact or inexact signal statistics, highlighting the impact of signal energy modeling accuracy. Based on that, we further proposed two practical energy modeling schemes for ULT of visual signals. Experimental results show that the proposed schemes improve the quality of reconstructed images by 3~5 dB, while reducing the signal modeling overhead from hundreds or thousands of meta data to only a few meta data. The perceptual quality of reconstruction is significantly improved.

A Novel Adaptive Scanning Approach for Effective H.265/HEVC Entropy Coding

The block transform video coder of H.265/HEVC has been formulated for a more flexible content representation to satisfy various implementation demands. Here three coefficient-scanning methods of diagonal, horizontal and vertical scan are employed for mapping a 2-D array to a 1-D vector for further reducing redundancy in entropy coding. However, the fixation scanning pattern does not fully exploit the correlation among quantized coefficients and the coding redundancy still exists. In this paper, a new adaptive coefficient scanning (ACS) method is proposed for effective H.265/HEVC entropy coding. Here the characteristic of syntax elements of quantized coefficients is first studied and then the related context probability of symbol is estimated through combining local property. Guided by the principle of entropy coding, the scanning approach is established for higher coding performance. Experimental results demonstrate that a reduction of about 3.6%–4.2% in bit-rate can be observed with almost no loss in coding complexity.

Improved Adaptive Transform for Residue in H.264/AVC Lossless Video Coding

The H.264/AVC was designed mainly for lossy video coding, the lossless coding of H.264 use bypass mode for DCT and quantization. Although sample-by-sample DPCM improves performance of coding, the benět is limited in intra. In this paper, a new adaptive transform is proposed based on the character of 4×4 block residual coefčient’s distribution, which can be used both in intra and inter coding. The greatest strength of the proposed transform is the decorrelation without in ation versus dynamic range of input matrix. Due to the random distribution of residual coefčcients, a specič transform is hard to play a positive impact on them. Therefore, several transforms of different directions will be implemented simultaneously, and the most efčient one will be determined by a proposed mechanism. Then, by means of statistic method, a new scan order is designed for CAVLC entropy encoder, cooperating with corresponding transform. The simulation results show that based on the fast algorithm of proposed method, the bit saving achieves about 7.41% bit saving in intra coding and 10.47% in inter, compared with H.264-LS.

An efficient encryption-then-compression technique for encrypted images using SVD

This paper presents a lossy compression technique for encrypted images using Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD) and Huffman coding. The core idea of the proposed technique lies in the selection of significant and less significant coefficients in the wavelet domain. Significant and less significant coefficients are encrypted using pseudo-random number sequence and coefficient permutation respectively. Furthermore, encrypted significant data is compressed by quantization and entropy coding while, less significant encrypted data is efficiently compressed by discarding irrelevant information using SVD and Huffman coding techniques. At receiver side, a reliable decompression and decryption technique is used to reconstruct the original image content with the help of compressed bit streams and secret keys. The performance of proposed technique is evaluated using parameters such as Compression Ratio (CR) and Peak-Signal-to-Noise Ratio (PSNR). Experimental results demonstrate the effectiveness of proposed work over prior work on compression of encrypted images and obtain the compression performance comparable to state of art work on compression of unencrypted images i.e. JPEG standard.

Direction-adaptive fixed length discrete cosine transform framework for efficient H.264/AVC video coding

The 2D-discrete cosine transform (2D-DCT) is one of the popular transformation for video coding. Yet, 2D-DCT may not be able to efficiently represent video data with fewer coefficients for oblique featured blocks. To further improve the compression gain for such oblique featured video data, this paper presents a directional transform framework based on direction-adaptive fixed length discrete cosine transform (DAFL-DCT) for intra-, and inter-frame. The proposed framework selects the best suitable transform mode from eight proposed directional transform modes for each block, and modified zigzag scanning pattern rearranges these transformed coefficients into a 1D-array, suitable for entropy encoding. The proposed scheme is analysed on JM 18.6 of H.264/AVC platform. Performance comparisons have been made with respect to rate-distortion (RD), Bjontegaard metrics, encoding time etc. The proposed transform scheme outperforms the conventional 2D-DCT and other state-of-art techniques in terms of compression gain and subjective quality.

Method for increasing resistance of cyberattacks for video content in infocommunication system

The importance of applying an video information resource (VIR) is substantiated. Indicates the need for ensuring the information security of the state video information resource. The impact of cyber threats to loss categories of information security of VIR is analyzed. It indicates the presence of problematic deficiencies for countering cyber attacks methods. The necessity of creating a method of increasing resistance VIR to DDoS-attacks in the info-communication systems in modes where cyber attacks are not de-tected or masked is proved. It indicates the presence of deficiencies of existing technologies for syntactic code representation of video content. It shows the presence the vulnerability of position for entropy coding. The structural-block coding is proposed to use to fix this vulnerability. The status categories of integrity and availability of video content in the presence of errors in the code is analyzed. To enhance the integrity of the information resource for a given degree of availability proposed to produce distribution of code in the structure of equal length. Examines the impact of the created method to the condition of information security video. It indicates an increase of the integrity of the information resource by using the created method.

Compression of Medical Images by Integer Wavelet Transform

In today’s world of information exchange, Image compression has become an important process. It helps in effective utilization of high speed network resources. Medical image compression has an important role in medical field because they are used for future reference of patients. Medical data is compressed in such a way so that no medical information is lost. Medical imaging poses the great challenge of having compression algorithms that reduce the loss of fidelity as much as possible so as not to contribute to diagnostic errors and yet have high compression rates for reduced storage and transmission time. The medical image needs to undergo the process of compression before storing and transmitting it. Firstly, predictive encoding of the pixel values is done then it is transformed using wavelet transform after that obtained variables are encoded by an entropy encoder.

Variable length integer codes based on radix number system conversion

A new coding scheme called radix conversion variable length code (RCVLC) is proposed to represent non-negative integers from small values to very large values. RCVLC obtains lowest code length to represent mid-range and large-range integers than Fibonacci code, Elias codes and other existing schemes. This RCVLC is applied as an entropy coder of the Burrows–Wheeler Transform data compressor to compress Calgary corpus which contains both binary and text files. It is observed from the experimental results that the new RCVLC obtains better compression than the existing coding schemes on an average.

Correlated Block Quad-Tree Segmented and DCT based Scheme for Color Image Compression

Background/Objectives: This paper presents a color image compression method to increase the compression ratio without affecting the original scene by noise or distortion. Methods/Analysis: In this paper an attempt to minimize data loss without highly affect the compression ratio by employing low lossy rate quad-tree compression technique to increase the correlation between pixels that will enhance DCT results and also compress the data before DCT phase, using Quantization and entropy encoders such as run length encoding and shift encoding will further compress the image. Finding: For conducted six different visual information images, the Compression Ratio (CR) results of the proposed method on average to be around 1:29 of the size of the original image, more compression ratio can be achieved by increasing the compression levels, this high compression ratio is considered a best ratio compared with the achieved Peak Signal to Noise Ratio (PSNR) of the decompressed-compressed image. Application/Improvements: This compression system can be used in Storing or Transforming Color Images due to its high compression ratio while the noise and distortion still as minimum as possible.

Fast-adaptive near-lossless image compression

The purpose of image compression is to store or transmit image data efficiently. However, most compression methods emphasize the compression ratio rather than the throughput. We propose an encoding process and rules, and consequently a fast-adaptive near-lossless image compression method (FAIC) with good compression ratio. FAIC is a single-pass method, which removes bits from each codeword, then predicts the next pixel value through localized edge detection techniques, and finally uses Golomb–Rice codes to encode the residuals. FAIC uses only logical operations, bitwise operations, additions, and subtractions. Meanwhile, it eliminates the slow operations (e.g., multiplication, division, and logarithm) and the complex entropy coder, which can be a bottleneck in hardware implementations. Besides, FAIC does not depend on any precomputed tables or parameters. Experimental results demonstrate that FAIC achieves good balance between compression ratio and computational complexity in certain range (e.g., peak signal-to-noise ratio >35 dB, bits per pixel>2). It is suitable for applications in which the amount of data is huge or the computation power is limited.

Fast entropy-based CABAC rate estimation for mode decision in HEVC.

High efficiency video coding (HEVC) seeks the best code tree configuration, the best prediction unit division and the prediction mode, by evaluating the rate-distortion functional in a recursive way and using a “try all and select the best” strategy. Further, HEVC only supports context adaptive binary arithmetic coding (CABAC), which has the disadvantage of being highly sequential and having strong data dependencies, as the entropy coder. So, the development of a fast rate estimation algorithm for CABAC-based coding has a great practical significance for mode decision in HEVC. There are three elementary steps in CABAC encoding process: binarization, context modeling, and binary arithmetic coding. Typical approaches to fast CABAC rate estimation simplify or eliminate the last two steps, but leave the binarization step unchanged. To maximize the reduction of computational complexity, we propose a fast entropy-based CABAC rate estimator in this paper. It eliminates not only the modeling and the coding steps, but also the binarization step. Experimental results demonstrate that the proposed estimator is able to reduce the computational complexity of the mode decision in HEVC by 9–23 % with negligible PSNR loss and BD-rate increment, and therefore exhibits applicability to practical HEVC encoder implementation.

A CU-Level Rate and Distortion Estimation Scheme for RDO of Hardware-Friendly HEVC Encoders Using Low-Complexity Integer DCTs.

In this paper, a low complexity coding unit (CU)-level rate and distortion estimation scheme is proposed for High Efficiency Video Coding (HEVC) hardware-friendly implementation where a Walsh-Hadamard transform (WHT)-based low-complexity integer discrete cosine transform (DCT) is employed for distortion estimation. Since HEVC adopts quadtree structures of coding blocks with hierarchical coding depths, it becomes more difficult to estimate accurate rate and distortion values without actually performing transform, quantization, inverse transform, de-quantization, and entropy coding. Furthermore, DCT for rate-distortion optimization (RDO) is computationally high, because it requires a number of multiplication and addition operations for various transform block sizes of 4-, 8-, 16-, and 32-orders and requires recursive computations to decide the optimal depths of CU or transform unit. Therefore, full RDO-based encoding is highly complex, especially for low-power implementation of HEVC encoders. In this paper, a rate and distortion estimation scheme is proposed in CU levels based on a low-complexity integer DCT that can be computed in terms of WHT whose coefficients are produced in prediction stages. For rate and distortion estimation in CU levels, two orthogonal matrices of 4×4 and 8×8 , which are applied to WHT that are newly designed in a butterfly structure only with addition and shift operations. By applying the integer DCT based on the WHT and newly designed transforms in each CU block, the texture rate can precisely be estimated after quantization using the number of non-zero quantized coefficients and the distortion can also be precisely estimated in transform domain without de-quantization and inverse transform required. In addition, a non-texture rate estimation is proposed by using a pseudoentropy code to obtain accurate total rate estimates. The proposed rate and the distortion estimation scheme can effectively be used for HW-friendly implementation of HEVC encoders with 9.8% loss over HEVC full RDO, which much less than 20.3% and 30.2% loss of a conventional approach and Hadamard-only scheme, respectively.

A Non-format Compliant Scalable RSA-based JPEG Encryption Algorithm

A non-format compliant JPEG encryption algorithm is proposed which is based on a modification of the RSA encryption system. Firstly, an alternate form of entropy coding is described, which is more suited to the proposed algorithm, instead of the zigzag coding scheme used in JPEG. The algorithm for the encryption and decryption process is then elaborated. A variant of the algorithm, also based on the RSA algorithm is also described, which is faster than the original algorithm, but expands the bit stream slightly. Both the algorithms are shown to be scalable and resistant to ‘sketch’ attacks. Finally, the encrypted file sizes for both the algorithms are compared with the unencrypted JPEG compressed image file size. The encrypted image is found to be moderately expanded, but which is justified by the high security and most importantly, the scalability of the algorithm.

Joint compression and encryption of visual data using orthogonal parametric transforms

Abstract In this paper, we introduce a novel method of joint compression and encryption of visual data. In the proposed approach the compression stage is based on block quantization while the encryption uses fast parametric orthogonal transforms of arbitrary forms in combination with a novel scheme of intra-block mixing of data vectors. Theoretical analysis of the method indicates no impact of encryption stage on the effectiveness of block quantization with an additional step of first order entropy coding. Moreover, a series of experimental studies involving natural images and JPEG lossy compression standard were performed. Here, the obtained results indicate a high level of visual content concealment with only a small reduction of compression performance. An additional analysis of security allows to state that the proposed method is resistant to cryptanalytic attacks known for visual data encryption schemes including the most efficient NZCA attack. The proposed method can be also characterized by high computational efficiency and feasibility of hardware realizations.