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The bulk of Internet interactions is highly redundant and also security sensitive. To reduce communication bandwidth and provide a desired level of security, a data stream is first compressed to squeeze out redundant bits and then encrypted using authenticated encryption. This generic solution is very flexible and works well for any pair of (compression, encryption) algorithms. Its downside, however, is the fact that the two algorithms are designed independently. One would expect that designing a single algorithm that compresses and encrypts (called compcrypt) should produce benefits in terms of efficiency and security. The work investigates how to design a compcrypt algorithm using the ANS entropy coding. First, we examine basic properties of ANS and show that a plain ANS with a hidden encoding table can be broken by statistical attacks. Next, we study ANS behavior when its states are chosen at random. Our compcrypt algorithm is built using ANS with randomized state jumps and a sponge MonkeyDuplex encryption. Its security and efficiency are discussed. The design provides 128-bit security for both confidentiality and integrity/authentication. Our implementation experiments show that our compcrypt algorithm processes symbols with a rate up to 269 MB/s (with a slight loss of compression rate) 178 MB/s.

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression.

This work develops a model-based compression scheme for seismic data. First, seismic traces are modeled as multitone sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Second, a parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. Next, the residuals are compressed using entropy coding or quantization coding techniques. The corresponding compression ratios are presented. Finally, the proposed model-based compression scheme is compared with the linear predictive coding (LPC) algorithm and the distributed principal component analysis (DPCA) algorithm on a real seismic database. The performance of the proposed model based is shown to be superior to that of the LPC and DPCA.

Geometric partitioning has attracted increasing attention by its remarkable motion field description capability in the hybrid video coding framework. However, the existing geometric partitioning (GEO) scheme in Versatile Video Coding (VVC) causes a non-negligible burden for signaling the side information. Consequently, the coding efficiency is limited. In view of this, we propose a spatio-temporal correlation guided geometric partitioning (STGEO) scheme to efficiently describe the object information in the motion field of video coding. The proposed method can economize the bits consumed for side information signaling, including the partitioning mode and motion information. We firstly analyze the characteristics of partitioning mode decision and motion vector selection in a statistically-sound way. Based on the observed spatio-temporal correlation, we design a mode prediction and coding method to reduce the overhead for representing the above mentioned side information. The main idea is to predict the STGEO modes and motion candidates that have higher selection possibilities, which can guide the entropy coding, i.e., representing the predicted high-probability modes and motion candidates with fewer bits. In particular, the high-probability STGEO modes are predicted based on the edge information and history modes of adjacent STGEO-coded blocks. The corresponding motion information is represented by the index in a merge candidate list, which is adaptively inferred based on the off-line trained merge candidate selection probability. Simulation results show that the proposed approach achieves 0.95% and 1.98% bit-rate savings on average compared to VTM-8.0 without GEO for Random Access and Low-Delay B configurations, respectively.

Screen contents have become a popular image type driven by the growing market for transferring display screen between devices, especially mobile devices. Due to the ultra-high quality display featured in most of nowadays mobile devices, lossless screen content coding (SCC) is usually required or preferred. Mobile devices also require ultra-low power consumption in all tasks including SCC. To address these issues, this paper proposes an ultra-low coding complexity technique based on string matching for high efficiency lossless SCC. The technique covers three major coding phases of fast searching, prediction, and entropy coding. Condensed hash table (CHT) based fast searching is proposed to speed-up reference string searching process. Coplanar prediction (CP) and predictor-dependent residual (PDR) are presented to first efficiently predict an unmatchable pixel using multiple neighboring pixels and then further reduce the entropy of prediction residuals. To achieve a good trade-off between coding complexity and efficiency, 4-bit-aligned variable length code (4bVLC) and byte-aligned multi-variable-length-code (BMVLC) are proposed to code the prediction residuals and three string matching parameters, respectively. For 184 screen content images commonly used, compared with X265 and PNG in the default configuration and lossless mode, the proposed technique achieves 35.67% less total compressed bytes with only 0.96% encoding and 1.54% decoding runtime, and 10.04% less total compressed bytes with only 6.83% encoding and 24.32% decoding runtime, respectively. The proposed technique also outperforms X265 and PNG in all other configurations. For twelve HEVC-SCC CTC images, compared with PNG in fast, default and slow configurations and X265 in ultrafast and default configurations, the proposed technique shows significant advantage with both high coding efficiency and ultra-low coding complexity.

We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent. Our formulation scales efficiently beyond the first order and is agnostic of the quantization scheme. The network can then be trained to minimize the entropy of the quantized parameters, so that they can be optimally compressed via entropy coding. We experiment with our entropy formulation at quantizing and compressing well-known network architectures over multiple datasets. Our approach compares favorably over similar methods, enjoying the benefits of higher order entropy estimate, showing flexibility towards non-uniform quantization (we use Lloyd-max quantization), scalability towards any entropy order to be minimized and efficiency in terms of compression. We show that HEMP is able to work in synergy with other approaches aiming at pruning or quantizing the model itself, delivering significant benefits in terms of storage size compressibility without harming the model’s performance.

Typical high efficiency video coding (HEVC) encoders use rate-distortion optimization (RDO) to select the best coding parameters among numerous candidates to achieve high compression efficiency. However, calculating the rate-distortion cost for each coding parameter entails high computational complexity, which limits the real-time application of these encoders. In this study, time-consuming processes, such as reconstruction and entropy coding, are removed during the calculation of the full RD cost to reduce the complexity of the encoder. Instead, RD costs are estimated for transformed coefficients by using the RD cost model, which is made up of a distortion model based on the distribution features of the transformed coefficients, a residual rate model based on the characteristics of the entropy coding module, and a simple and convenient header rate model. In addition to the full RD cost, typical encoders also adopt fast RD cost, which is composed of the sum of absolute transform differences (SATD) and header bit. To further reduce encoding complexity, three simplified SATD (SSATD) methods based on DCT and DST features are proposed to serve as substitutes for SATD in fast RD cost, thus reducing the computational complexity of SATD calculation. Experimental results show that 35.53%, 14.57%, and 14.37% of coding time can be reduced respectively under AI, RA and LDP configurations with negligible RD performance loss when applying the proposed RD cost model and the SSATD methods to HM16.7. Besides, the proposed methods also can reduce 4%–9% of encoding time and exhibits the same RD performance as state-of-the-art methods.

Electrocardiogram (ECG) is used to record the electrical activity of heart. If the instrument monitoring the signal for a long time, it will produce large amount of data. So, the effective lossless ECG compression system can help to reduce the storage space. This brief presents a hardware architecture of multi-channel lossless ECG compression system. The system is based on the algorithm including multi-channel linear prediction and adaptive linear prediction. Also, Golomb rice code (GRC) is used for entropy coding. The hardware implementation has been designed with low hardware complexity usage while using optimum hardware resources. And the architecture can process multiple channels in parallel that can obtain the high throughput. PTB database has been used for verification and testing purposes. This design was implemented in TSMC 180nm. The implementation results show the gate count is 476K and power consumption is $69.18\mu \text{W}$ while working frequency is 1 KHz.

The fast JPEG image compression algorithm is a requisite in many applications such as high-speed video measurement systems and digital cinema. Many existing methods have implemented the JPEG compression in parallel based on GPU except for entropy coding, which is a variable-length coding method and seems like a better fit for sequential implementation. However, entropy coding is an essential part of the JPEG compression system and typically takes up a large proportion of the time when implemented on the CPU. To tackle this problem, we propose an efficient parallel entropy coding (EPEnt) method for parallel JPEG compressing. The proposed method conducts entropy coding in three parallel steps: coding, shifting, and stuffing. Specifically, according to the different characteristics of image components, we devise thread-based and warp-based functions in the coding stage to further improve the efficiency under guaranteeing image quality, respectively. We apply the proposed method to the parallel JPEG compression system and evaluate the performance based on compute unified device architecture (CUDA). The experimental results demonstrate that compared with sequential implementation, the maximum speedup ratio of entropy coding can reach 39 times without affecting compressed images quality. Meanwhile, the whole JPEG compression process efficiency increases by at least 28% compared with state-of-the-art parallel methods in terms of speedup ratio.

Existing 3-D point cloud attribute compression methods are rigid for complex signals defined in spatially irregular domain, as they solely depend on geometric information or adopt pre-defined analytic bases for transform. In this article, we propose a multi-scale structured dictionary learning algorithm for 3-D point cloud attribute compression. The proposed multi-scale dictionary leverages hierarchical sparsity within signals by adapting atoms in an arborescent structure. It supports hierarchical sparse transform for encoding voxelized point cloud attributes with progressive refinement of high-frequency details from coarser to finer scales. To represent spatial blocks with varying amounts of occupied voxels, a non-uniform binary weight matrix is incorporated in dictionary learning to characterize the dimensional irregularity of signals. To guarantee optimal approximation with improved efficiency, alternating optimizations including Alternating Direction Method of Multipliers and Gauss-Seidel iterations are adopted for the weighted mixed $\ell _{2}/\ell _{1}$ -minimization. Furthermore, we develop a lossy attribute compression framework by integrating sparse transform based prediction with dedicated quantization and entropy coding routines. Experimental results demonstrate that the proposed framework outperforms the state-of-the-art transform-based coding methods and is competitive with the most recent MPEG PCC test models in point cloud attribute compression.

Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

Pre-processing of medical images plays a vibrant part in the field of medicine to detect patient’s disease at an earlier stage. Hospitals and medical centers generate an enormous volume of digital medical images day by day, which is used for several purposes of diagnostic procedures. Because of many images,for secured transmission, image compression is required to reduce the redundancies in the image and to accomplish the proficient image communication. To reduce the redundancies in the image and to accomplish the proficient communication of images. A competent Adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning (ASFSCSLEC-DNL) technique is presented to develop the image encryption and compression. The main goal of the ASFSCSLEC-DNL technique was to improve the security level of medical image transmission. The deep feed-forward artificial neural network was applied in the ASFSCSLEC-DNL technique for medical image pre-processing, encryption, and compression with multiple layers. The adaptive sigma filter was employed to denoise the medical image. The medical image encryption and signature generation were done with synorr certificateless signcryption. Finally, Levenshtein entropy encoding was applied to compress images. Then the compressed image was sent to the receiver where the decompression and decryption are implemented using Levenshtein entropy decoding and synorr certificateless decryption. Investigational estimation was carried out in chest X-ray medical images and the results of ASFSCSLEC-DNL technique proved more capable in terms of higher peak signal to noise ratio and compression ratio with lesser encryption time compared to the existing state-of-the-art methods.

Entropy coding is the essential block of transform coders that losslessly converts the quantized transform coefficients into the bit‐stream suitable for transmission or storage. Usually, the entropy coders exhibit less compression capability than the lossy coding techniques. Hence, in the past decade, several efforts have been made to improve the compression capability of the entropy coding technique. Recently, a symbol reduction technique (SRT) based Huffman coder is developed to achieve higher compression than the existing entropy coders at similar complexity of the regular Huffman coder. However, the SRT‐based Huffman coding is not popular for the real‐time applications due to the improper negative symbol handling and the additional indexing issues, which restrict its compression gain at most 10–20% over the regular Huffman coder. Hence, in this paper, an improved SRT (ISRT) based Huffman coder is proposed to properly alleviate the deficiencies of the recent SRT‐based Huffman coder and to achieve higher compression gains. The proposed entropy coder is extensively evaluated on the ground of compression gain and the time complexity. The results show that the proposed ISRT‐based Huffman coder provides significant compression gain against the existing entropy coders with lower time consumptions.

This paper is about a novel representation based on lossless encoding for medical images. It relies on directional codes for efficient storage and transmission. Image compression is a recent research, where removal or elimination of redundant information is carried out in an image. In this paper, for medical image compression, an edge- and texture-based entropy coding is presented. The proposed work is partitioned into two stages. In the first stage, a novel lossless hex-directional chain code or chain code 16 (C16) boundary representation approach is applied for edge encoding. In this C16 method, a set of pixels are connected along the edge of an image based on the direction code. Eliminating redundant information and retaining the topological information is the basic idea of chain code. It concerns the shape and the structure of the object which helps in preserving the quality of an image. In the second stage, predictor-based entropy coding is applied for adopting texture feature as a basic primitive. Finally, the outcome of the two stages is provided to the entropy coder for attaining compression. The proposed method is tested with standard benchmark chain code dataset and medical image datasets for various modalities of different sizes. Performance metrics such as compression ratio (%), encoding time and mean square error are used to evaluate the proposed technique. From the results attained, it is evident that the proposed technique results in better compression performance when compared to the existing methods.

End-to-end image compression using trained deep networks as encoding/decoding models has been developed substantially in the recent years. Previous work is limited in using a single encoding/decoding model, whereas we explore the usage of multiple encoding/decoding models as an ensemble. We propose several methods to obtain multiple models. First, we adopt the boosting strategy to train multiple networks with diversity as an ensemble. Second, we train an ensemble of multiple probability distribution models to reduce the distribution gap for efficient entropy coding. Third, we present a geometric transform-based self-ensemble method. The multiple models can be regarded as the multiple coding modes, similar to those in non-deep video coding schemes. We further adopt block-level model/mode selection at the encoder side to pursue rate-distortion optimization, where we use hierarchical block partitioning to improve the adaptation ability. Compared with single-model end-to-end compression, our proposed method improves the compression efficiency significantly, leading to 21% BD-rate reduction on the Kodak dataset, without increasing the decoding complexity. On the other hand, when keeping the same compression efficiency, our method can use much simplified decoding models, where the floating-point operations are reduced by 70%.

Explosive growth of data in digital world leads to the requirement of efficient technique to store and transmit data. Due to limited resources, data compression (DC) techniques are proposed to minimize the size of data being stored or communicated. As DC concepts results to effective utilization of available storage area and communication bandwidth, numerous approaches were developed in several aspects. In order to analyze how DC techniques and its applications have evolved, a detailed survey on many existing DC techniques is carried out to address the current requirements in terms of data quality, coding schemes, type of data and applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their characteristics, underlying concepts, experimental factors and limitations. Finally, this paper insight to various open issues and research directions to explore the promising areas for future developments.

The proposed research work is presenting a novel approach in the field of steganography, especially in the compressed video domain with optimum imperceptibility to secure the secret information. In this approach, the specific secret cover video frames are selected from the sequence of video frame from which the non-dynamic region is separated. Discrete sine transform (DST) transforms this non-dynamic region from spatial domain to frequency domain. The least significant bits (LSBs) of the integer part of DST components are used to conceal the secret data. The H.264 codec is used to construct the compressed stego video using intra-frame, inter-frame prediction, motion vector estimation, transform coefficient, i.e., Discrete Cosine Transform (DCT), quantization, and entropy coding. The efficiency of the proposed hybrid technique “DST- Secret Bit Positions of Non-dynamic Region for Message (DST-SBPNRM)” for video steganography is measured by evaluating imperceptibility, robustness and embedding capacity. Moreover, the proposed technique is experimented on the well-defined video dataset and the obtained results are compared with the related work to validate the significance of the proposed work.

The ever-increasing demand for high-quality digital video requires efficient compression techniques and fast video codecs. It necessitates increased complexity of the video codec algorithms. So, there is a need for hardware accelerators to implement such complex algorithms. The latest video compression algorithms such as High-Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) have been adopted Context-based Adaptive Binary Arithmetic Coding (CABAC) as the entropy coding method. The CABAC has two main data processing paths: regular and bypass bin path, which can achieve good compression when used with Syntax Elements (SEs) statistics. However, it is highly intrinsic data dependence and has sequential coding characteristics. Thus, it is challenging to parallelize. In this work, a 6-core bypass bin path having high-throughput and low hardware area has been proposed. It is a parallel architecture capable of processing up to 6 bypass bins per clock cycle to improve throughput. Further, the resource-sharing techniques within the binarization and a common controller block have reduced the hardware area. The proposed architecture has been simulated, synthesized, and prototyped on 28 nm Artix 7 Field Programmable Gate Array (FPGA). The implementation of Application Specific Integrated Circuit (ASIC) has been done using 65 nm CMOS technology. The proposed design achieved a throughput of 1.26 Gbin/s at 210 MHz operating frequency with a low hardware area compared to existing architectures. This architecture also supports multi-standard (HEVC/VVC) encoders for Ultra High Definition (UHD) applications.

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

The new version of the Context-based Adaptive Binary Arithmetic Coding (CABAC) that is used in the latest Versatile Video Coding (VVC) technology is the state-of-the-art in the field of entropy compression of video data. This paper gives an in-depth analysis of possible, further extensions of the CABAC technique for even more accurate estimation of the probabilities of coded data symbols. The paper considers four author’s examples of such extensions. With respect to these examples, the paper shows the practical limitations of further increasing the efficiency of the newest version of CABAC used in VVC. Results show that for the considered methods of improvements the limit of gain in compression is up to 0.2073% on average, when compressing the VVC video data.

This article presents an extensive review of the state-of-the-art system-level solutions featuring complexity reduction and/or dedicated hardware designs for the AV1 and VVC video coding formats. These formats introduced several novel coding techniques compared to their predecessors to improve the coding efficiency at the cost of a significant computational cost. In this article, we discuss the main novelties of AV1 and VVC in each coding module, including block partitioning, intra and inter prediction, transform, entropy coding, and in-loop filters. Then, we present the main published works focusing on complexity reduction and hardware designs for AV1 and VVC. Most of the complexity reduction solutions target the complex and flexible block partitioning structures of these encoders to provide a better tradeoff between coding efficiency and complexity reduction whereas the hardware designs focus on the challenge of implementing the new coding tools to attend real-time processing of high-definition videos. Even with the presented works reaching impressive results, these research fields remain opened for innovative contributions, as discussed in this article.

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

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

When a Synthetic Aperture Radar (SAR) acquires raw data using a satellite or airborne platform, it must be transferred to the ground for further processing. For example, SAR raw data need a so-called 'focusing' signal processing to render it into a visible image. Such processing is time and computing consuming, and it is commonly carried out in computing centres. Since the data transfer rate is a typical limitation when communicating with the ground station, compression is necessary to reduce transmission time. So far, this procedure has been implemented in application-specific hardware, but recent adoption of avionic computational GPUs opened to new high-performance onboard perspectives. Due to the limited availability of avionic GPUs, we focused on parallel performance estimation starting from measures relative to a similar off-the-shelf solution. In this paper, we present a GPU algorithm for raw SAR data compression, which uses 1-dimensional DCT transforms, followed by quantisation and entropy coding. We evaluate results using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 data by measuring compression rates, statistical parameters, and distortion on decompressed and then focused images. Moreover, by evaluating the Algorithmic Overhead induced by the parallelisation strategy, we predict the best thread-block configuration for possible adoption of such a GPU algorithm on one of the most available avionic hardware. (c) 2020 Elsevier B.V. All rights reserved.

Arithmetic Coding (AC) is a form of entropy encoding used in lossless data compression. It is a well-known, state of the art technique, in which the frequently seen symbols are encoded with fewer bits than rarely seen symbols. It has been widely used since last four decades. Many researchers worked on it to improve its performance and they successfully experimented with it. This technique has also been in use in combination with other techniques to gain surprising results. In this survey paper, an effort is made to recap a number of accomplishments from 1976 to 2017 regarding Arithmetic Coding. This study provides an insight for new researchers to know how this technique evolved with time and how major achievements were made using this technique. This paper gives a comparison of AC with another well-known technique named Huffman Coding. Comparison with its contemporary counterparts shows that it is better in performance almost in every situation.

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.

In view of the limited bandwidth of underwater video image transmission, a low bit rate underwater video compression coding method is proposed. Based on the preprocessing process of wavelet transform and coefficient down-sampling, the visual redundancy of underwater image is removed and the computational coefficients and coding bits are reduced. At the same time, combined with multi-level wavelet decomposition, inter frame motion compensation, entropy coding and other methods, according to the characteristics of different types of frame image data, reduce the number of calculations and improve the coding efficiency. The experimental results show that the reconstructed image quality can meet the visual requirements, and the average compression ratio of underwater video can meet the requirements of underwater acoustic channel transmission rate.

An entropy coder takes as input a sequence of symbol identifiers over some specified alphabet and represents that sequence as a bitstring using as few bits as possible, typically assuming that the elements of the sequence are independent of each other. Previous entropy coding methods include the well-known Huffman and arithmetic approaches. Here we examine the newer asymmetric numeral systems (ANS) technique for entropy coding and develop mechanisms that allow it to be efficiently used when the size of the source alphabet is large—thousands or millions of symbols. In particular, we examine different ways in which probability distributions over large alphabets can be approximated and in doing so infer techniques that allow the ANS mechanism to be extended to support large-alphabet entropy coding. As well as providing a full description of ANS, we also present detailed experiments using several different types of input, including data streams arising as typical output from the modeling stages of text compression software, and compare theproposed ANS variants with Huffman and arithmetic coding baselines, measuring both compression effectiveness and also encoding and decoding throughput. We demonstrate that in applications in which semi-static compression is appropriate, ANS-based coders can provide an excellent balance between compression effectiveness and speed, even when the alphabet is large.

Editor’s note : The usage of multicore architectures for 3D-HEVC intraframe prediction is of great benefit, especially to improve the performance, throughput, and even the energy consumption in systems. Multicore hardware is used in all high-performance image processing applications, and also in mobile devices such as cellphones. The distribution of the algorithm to different parallel processor architecture is discussed and evaluated in this article.-- ichael Hubner, Brandenburg University of Technology

In High Efficiency Video Coding (HEVC), multiple-QP (quantization parameter) optimization can adapt to a local video content. However, the multiple-QP implementation in the HEVC reference software (HM 16.6) achieves the best QP value for each coding block with a large amount of computational complexity. To address this challenge, we propose a fast rate-constrained multiple-QP optimization approach for the HM platform. We first introduce a template-based transform coefficient selection method which can save the overall complexity of entropy coding. In addition, we model the multiple-QP determination as a new rate-constrained optimization problem, and finally, we get a feasible solution with a lower computation overhead. Experimental results show that our method dramatically reduces the average complexity under the all-intra, low-delay and random-access configuration.

This paper describes a video coding technology proposal submitted by MediaTek in April 2018 in response to a joint call for proposals (CfP) issued by ITU-T VCEG and ISO/IEC MPEG. The proposal uses the conventional block-based hybrid coding approach with a breakthrough design of block structure, substantial improvements of coding tools, and new inventions of loop filtering techniques. First, the block structure is designed using a highly flexible partitioning scheme, where each coding tree unit in a picture is split into multiple coding units (CUs) by a recursive quaternary tree followed by a recursive binary-ternary tree. The prediction unit (PU) and transform unit (TU) concepts are unified with the CU concept, i.e., PU and TU are basically the same as CU. Moreover, inter prediction, intra prediction, transform, quantization, in-loop filtering, and entropy coding are all improved. Finally, new loop filtering techniques are invented, where the proposed convolutional neural network loop filtering is of particular interest. The proposed video codec achieves 43.81% average bit rate savings based on objective measures compared to the High Efficiency Video Coding (HEVC) anchors and is among the best-performing CfP responses both objectively and subjectively. It has been partially adopted into the working draft of the Versatile Video Coding (VVC) standard.