Entropy Coding Research Articles

JPEG image encryption with grouping coefficients based on entropy coding

In the existing JPEG image encryption schemes, the block feature values that can be used to reduce key search space are either difficult to be changed or need to be changed through overflow processing which leads to low generality and high encryption runtime. To change block feature values without overflow processing, a novel JPEG image encryption scheme is proposed. For AC encryption, the complete and end AC groups based on undivided RSV (run/size, value) (ACG-URSV) are permuted separately to change different features. Complete ACG-URSV containing different number of RSVs is used to change the non-zero coefficients count (NCC) and energy of AC coefficients (EAC). End ACG-URSV containing the zero coefficients after position of last non-zero AC coefficient (PLZ) is mainly used to change PLZ. Besides, the intra-block RSV permutation and block permutation are used to further destroy correlation. For DC encryption, the positive DC prediction error (PDC-PE) groups modulo encryption is proposed to avoid overflow processing. The experimental results show that this paper reduces encryption runtime by more than half, improves generality by at least 20%. When quality factor of 90, the average change rates of NCC, EAC and PLZ values are increased by 96.28%, 11.68% and 29.15%, respectively.

Efficient compression at the edge for real-time data acquisition in a billion-pixel X-ray camera

Recent advances in radiation detectors have significantly improved the resolution and frame rate of X-ray imaging systems at the cost of large data throughputs that can be challenging to transfer, store, and process. Such is the case of the billion-pixel X-ray camera, where the estimated throughput of 10 terabytes per second (TB/s) exceeds the capabilities of current data acquisition systems (DAQs) for real-time transfer and processing. To address this, we propose a lossy, multi-stage compression scheme to reduce data at the source. This work leverages machine learning (ML) to produce sparse representations of the camera’s images, followed by quantization and entropy coding for compression. The proposed scheme achieves a 100:1 compression ratio with high PSNR and SSIM scores on X-ray source images. Finally, we implement the sparse coding neural network (NN) on an FPGA to showcase the feasibility, low power, and low latency of edge data compression.

MASIC: Deep Mask Stereo Image Compression

Stereo image compression (SIC) aims to simultaneously compress a pair of left and right stereoscopic images, which can achieve higher compression efficiency than single image compression. In this paper, to benefit the SIC tasks, we collect a large real-world stereo image dataset, namely Palace, which is composed of hundreds of stereo image pairs at high-resolution. More importantly, we propose a novel mask stereo image compression network, namely MASIC, which can jointly compress the stereo images with high compression efficiency. Specifically, we first estimate the homography matrix between the stereo images through a regression model. Then, the left image is spatially transformed by the homography matrix, so that only the residual information needs to be encoded for the right image. To avoid the wrong guidance between stereo image pair, we propose a mask prediction module (MPM) to generate a multi-channel guided mask to navigate both the encoding and decoding processes. Based on the guided mask, we introduce a new mask conditional stereo entropy (MCSE) model, to fully explore the correlation between the stereo images in entropy coding. In the decoder, we develop a stereo decoding module to simultaneously decode the stereo images and enhance their compression quality. Experimental results show that our MASIC significantly advances the performance of SIC both quantitatively and qualitatively on a variety of datasets, and is robust to the change of parallax level between stereo images. The software codes are available at https://github.com/eecoder-dyf/MASIC.

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems.

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding.

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

Concurrent encryption and lossless compression using inversion ranks

For secure and efficient transmission or storage, data files are commonly compressed and encrypted. In this work, we introduce a cost-effective encryption method of files as a built-in component of a lossless compression algorithm, thus avoiding the added cost of employing two separate processes. We have shown in earlier studies that preprocessing data with Burrows–Wheeler Transformation followed by Inversion Ranking transformation in advance of the utilization of an entropy coder resulted in an extremely effective general-purpose lossless compression technique. In our proposed algorithm, we isolate the inversion frequency vector obtained from the Inversion Ranking transformation and transmit the compressed file. Since the inversion frequency vector is necessary for decompression, we use it as our secret key and transmit it over a secure channel. Compared to the size of a data file, its frequency vector is rather small. Thus, encrypting only the inversion frequency vector, instead of the entire compressed file, results in substantial savings in computational cost. We demonstrate that the proposed technique is effective and resistant to common attacks on image and audio data sets.

Point Cloud Compression Based on Joint Optimization of Graph Transform and Entropy Coding for Efficient Data Broadcasting

Point Cloud Compression Based on Joint Optimization of Graph Transform and Entropy Coding for Efficient Data Broadcasting

Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encoding

Gas-liquid two-phase flow is closely related to the production and transportation of energy industries. A flow pattern analysis and identification method based on multi-scale power spectral entropy (MPSE) with pseudo-image encoding (PIE) is proposed. The resistance sensor array is used to collect gas-liquid two-phase flow information from the vertical upward pipeline, and the data is dimensionally reduced to simplify analysis. The MPSE is derived from the power spectrum of reduced-dimensional sequences to characterize the frequency domain complexity of the flow system at different scales. The optimal scale for spectral entropy analysis is further calculated to investigate the transition mechanism of gas-liquid two-phase flow patterns. In addition, the PIE algorithm is used to map the power spectrum into two-dimensional grayscale images to visualize the differences in frequency domain features of flow patterns. Combined with the deep learning model ResNeXt, the flow patterns are classified and compared with the traditional identification scheme. Results show that the proposed method can reveal the dynamic evolution behavior of gas-liquid two-phase flow from the frequency domain and achieve accurate identification of the flow patterns.

Subject Review: Various Techniques for Image Compression

Due to the quick development of multimedia technology, now can see the increasing amount of multimedia data that is being generated or transmitted over the internet. digital image is one form of multimedia data, that can take different formats and different sizes. And here comes the main objective of image compression in transmitting or storing the data in an effective manner by decreasing the redundant or irrelevant information without significant loss of visual quality in image data. Compression of the images is utilized in various services, including TV broadcasts, satellite imagery, martial communications, webinars, medical imaging, weather reporting, etc. This paper provides a survey of the main techniques in image compression, covering both lossy & lossless approaches. The techniques that depend on lossy compression lose some of the image details during compression, while lossless compression techniques keep the image information without losing. Vector and scalar Quantization, transform coding, block truncation Coding, etc. are all examples of lossy approaches. Run length coding, entropy encoding, statistical coding, etc. are all examples of lossless techniques. This paper will assist the researchers in learning about these techniques and choosing appropriate techniques for their work.

A High-Performance Dual-Context MQ Encoder Architecture Based on Extended Lookup Table

Multiple quantization (MQ) encoder is a basic entropy encoder widely used in multimedia encoding systems, especially in JPEG2000 and JBIG2, which play a key role and make outstanding contributions to digital imaging. However, due to its strict serial working characteristics, the MQ encoder has become a key bottleneck restricting the optimization of JPEG2000 encoding performance. Many single ejection encoder architectures use pipeline staging to improve the throughput, but they have all reached their limits. A high-performance MQ encoder based on a novel lookup table is proposed to overcome this bottleneck, which improves the throughput while utilizing reasonable hardware resources and with low-power consumption. Experimental results show that the proposed encoder architecture can encode two symbols per cycle with a throughput of 1204.82 MSymbol/s, and it has a power consumption of only 11.05 mW. Compared with the conventional multi-context coder, the hardware utilization is reduced by 26%, and the hardware utilization efficiency is improved by 16%. Compared with the conventional single-context coder, the throughput is improved by 14.3%, and the power consumption is reduced by 40.1%.

Efficient image compression method using image super-resolution residual learning network

With the rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1).

A Highly Pipelined and Highly Parallel VLSI Architecture of CABAC Encoder for UHDTV Applications.

Recently, specifically designed video codecs have been preferred due to the expansion of video data in Internet of Things (IoT) devices. Context Adaptive Binary Arithmetic Coding (CABAC) is the entropy coding module widely used in recent video coding standards such as HEVC/H.265 and VVC/H.266. CABAC is a well known throughput bottleneck due to its strong data dependencies. Because the required context model of the current bin often depends on the results of the previous bin, the context model cannot be prefetched early enough and then results in pipeline stalls. To solve this problem, we propose a prediction-based context model prefetching strategy, effectively eliminating the clock consumption of the contextual model for accessing data in memory. Moreover, we offer multi-result context model update (MCMU) to reduce the critical path delay of context model updates in multi-bin/clock architecture. Furthermore, we apply pre-range update and pre-renormalize techniques to reduce the multiplex BAE's route delay due to the incomplete reliance on the encoding process. Moreover, to further speed up the processing, we propose to process four regular and several bypass bins in parallel with a variable bypass bin incorporation (VBBI) technique. Finally, a quad-loop cache is developed to improve the compatibility of data interactions between the entropy encoder and other video encoder modules. As a result, the pipeline architecture based on the context model prefetching strategy can remove up to 45.66% of the coding time due to stalls of the regular bin, and the parallel architecture can also save 29.25% of the coding time due to model update on average under the condition that the Quantization Parameter (QP) is equal to 22. At the same time, the throughput of our proposed parallel architecture can reach 2191 Mbin/s, which is sufficient to meet the requirements of 8 K Ultra High Definition Television (UHDTV). Additionally, the hardware efficiency (Mbins/s per k gates) of the proposed architecture is higher than that of existing advanced pipeline and parallel architectures.

An efficient image compression architecture based on optimized 9/7 wavelet transform with hybrid post processing and entropy encoder module

Satellite communication is popular due to the advancement in storing and transmitting satellite images. Image compression can be defined as the science of lowering the number of bits necessary to represent an image. In satellite image processing, image degradation is considered a challenging problem. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is the transformation-dependent image compression method developed especially for usage in on-board space platforms. It holds de-correlation, quantization and entropy encoding phases. This paper introduces a new optimized memory organization for Discrete Wavelet Transform (DWT) to perform spatial de-correlation with fewer memory requirements on an FPGA device. Also, the proposed optimized DWT is integrated with a hybrid post-processing and entropy encoder module to reduce the spatial redundancies between the wavelet coefficients and compress the de-correlated data with high compression performance. A high-throughput hardware implementation of the Binary arithmetic entropy Coder (BAEC) is also provided to perform lossless compression with low implementation complexity. To provide the convenience and compactness, the proposed system will be implemented in the Xilinx working platform by developing a Verilog code. The proposed model is then evaluated on the Arty Z7–20 development board. The proposed approach is analyzed on different performance parameters such as throughput and frequency. The proposed design allowed a maximum operating frequency of 250 MHz, leading to a throughput of 156.25 Msamples/sec on Zynq. In addition, the structure of the proposed method overtakes the conventional design in terms of memory requirements and area.

Compression of color digital hologram using wavelet thresholds and two-times quantization

An improved compression method of color digital hologram by using wavelet transform is proposed, and the image without color aliasing and distortion can be reconstructed from compressed digital hologram. With the presented method, wavelet decomposition, threshold, and quantization are used in the spectrum of hologram to reduce redundancy, then quantization and entropy coding are used in the spatial domain of hologram to remove coding redundancy further. Three different threshold methods for decomposition coefficients are used, and the high compression ratio as 83, 75 and 87 can be obtained respectively with the encoded color digital hologram. Meanwhile, the quality of the images reconstructed from digital hologram is minimally lost visually.

Local Geometry-Based Intra Prediction for Octree-Structured Geometry Coding of Point Clouds

Point cloud compression (PCC) is crucial for efficient and flexible storage as well as feasible transmission of point clouds in practice. For geometry compression, one popular approach is the octree-based solution. The intra prediction mechanism utilizes the spatial correlation in the static point cloud to predict the occupancy bit of the octree node for entropy coding, reducing the spatial redundancy. In this study, two local geometry-based prediction methods are proposed following statistical and theoretical analyses: binary prediction, which outputs the binary state (i.e., occupied or unoccupied), and ternary prediction, which provides a third option other than occupied or unoccupied (i.e., not predicted). In comparison to the state-of-the-art, the proposed binary prediction offers the Bjontegaard delta rate (BD-rate) of −0.8% for lossy compression and the bits per input point (bpip) of 100.09% for lossless compression in average, respectively. The binary prediction reduces the computational complexity in terms of more than 20% decrease in decoding time. In particular, it also provides noticeable reduction of the memory usage during entropy coding. The proposed ternary prediction provides −1.2% BD-rate for lossy compression and 97.19% bpip for lossless compression in average, respectively, in comparison to the state-of-the-art. While achieving performance gain, it is considerably more computational efficient by saving about 18% decoding time. Due to these advantages, part of the proposed ternary prediction has been adopted by the ongoing MPEG standard of geometry-based point cloud compression (G-PCC).

An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.

Lossless Recompression of JPEG Images Using Transform Domain Intra Prediction.

JPEG, which was developed 30 years ago, is the most widely used image coding format, especially favored by the resource-deficient devices, due to its simplicity and efficiency. With the evolution of the Internet and the popularity of mobile devices, a huge amount of user-generated JPEG images are uploaded to social media sites like Facebook and Flickr or stored in personal computers or notebooks, which leads to an increase in storage cost. However, the performance of JPEG is far from the state-of-art coding methods. Therefore, the lossless recompression of JPEG images is urgent to be studied, which will further reduce the storage cost while maintaining the image fidelity. In this paper, a hybrid coding framework for the lossless recompression of JPEG images (LLJPEG) using transform domain intra prediction is proposed, including block partition and intra prediction, transform and quantization, and entropy coding. Specifically, in LLJPEG, intra prediction is first used to obtain a predicted block. Then the predicted block is transformed by DCT and then quantized to obtain the predicted coefficients. After that, the predicted coefficients are subtracted from the original coefficients to get the DCT coefficient residuals. Finally, the DCT residuals are entropy coded. In LLJPEG, some new coding tools are proposed for intra prediction and the entropy coding is redesigned. The experiments show that LLJPEG can reduce the storage space by 29.43% and 26.40% on the Kodak and DIV2K datasets respectively without any loss for JPEG images, while maintaining low decoding complexity.

Fast Compressed Segmentation Volumes for Scientific Visualization.

Voxel-based segmentation volumes often store a large number of labels and voxels, and the resulting amount of data can make storage, transfer, and interactive visualization difficult. We present a lossless compression technique which addresses these challenges. It processes individual small bricks of a segmentation volume and compactly encodes the labelled regions and their boundaries by an iterative refinement scheme. The result for each brick is a list of labels, and a sequence of operations to reconstruct the brick which is further compressed using rANS-entropy coding. As the relative frequencies of operations are very similar across bricks, the entropy coding can use global frequency tables for an entire data set which enables efficient and effective parallel (de)compression. Our technique achieves high throughput (up to gigabytes per second both for compression and decompression) and strong compression ratios of about 1% to 3% of the original data set size while being applicable to GPU-based rendering. We evaluate our method for various data sets from different fields and demonstrate GPU-based volume visualization with on-the-fly decompression, level-of-detail rendering (with optional on-demand streaming of detail coefficients to the GPU), and a caching strategy for decompressed bricks for further performance improvement.

Comprehensive Comparisons of Uniform Quantization in Deep Image Compression

In deep image compression, uniform quantization is applied to latent representations obtained by using an auto-encoder architecture for reducing bits and entropy coding. Quantization is a problem encountered in the end-to-end training of deep image compression. Quantization’s gradient is zero, and it cannot backpropagate meaningful gradients. Many methods have been proposed to address the approximations of quantization to obtain gradients. However, there have not been equitable comparisons among them. In this study, we comprehensively compare the existing approximations of uniform quantization. Furthermore, we evaluate possible combinations of quantizers for the decoder and the entropy model, as the approximated quantizers can be different for them. We conduct experiments using three network architectures on two test datasets. The experimental results reveal that the best approximated quantization differs by the network architectures, and the best approximations of the three are different from the original ones used for the architectures. We also show that the combination of quantizers that uses universal quantization for the entropy model and differentiable soft quantization for the decoder is a comparatively good choice for different architectures and datasets.

Efficient and Compact Representations of Deep Neural Networks via Entropy Coding

Efficient and Compact Representations of Deep Neural Networks via Entropy Coding