Scalable Compression Research Articles

An autoencoder compression approach for accelerating large-scale inverse problems

Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer

The famous, yet unsolved, Fermi-Hubbard model for strongly-correlated electronic systems is a prominent target for quantum computers. However, accurately representing the Fermi-Hubbard ground state for large instances may be beyond the reach of near-term quantum hardware. Here we show experimentally that an efficient, low-depth variational quantum algorithm with few parameters can reproduce important qualitative features of medium-size instances of the Fermi-Hubbard model. We address 1 × 8 and 2 × 4 instances on 16 qubits on a superconducting quantum processor, substantially larger than previous work based on less scalable compression techniques, and going beyond the family of 1D Fermi-Hubbard instances, which are solvable classically. Consistent with predictions for the ground state, we observe the onset of the metal-insulator transition and Friedel oscillations in 1D, and antiferromagnetic order in both 1D and 2D. We use a variety of error-mitigation techniques, including symmetries of the Fermi-Hubbard model and a recently developed technique tailored to simulating fermionic systems. We also introduce a new variational optimisation algorithm based on iterative Bayesian updates of a local surrogate model.

Scalable compression for machine and human vision tasks via multi-branch shared module

The existing image compression methods are mainly aimed at human perception tasks instead of machine vision tasks. Rich features learned by the shallow and deep layers of pre-trained visual geometry group (VGG)-net can serve human perception and machine vision tasks, respectively. To improve the machine analysis capabilities of the human-targeted compression method, we propose a scalable image compression framework at low bit-rates. Specifically, the scalable compression framework is composed of a base layer (BL) and an enhancement layer (EL), which utilize the correlation between the above features to perform machine analysis and human perception tasks, respectively. For effectively utilizing the above two types of features, a multi-branch shared module that utilizes the complementarity of multi-branch convolution kernels to retain compact information for the above features to support BL and EL tasks is proposed. In addition, for further improving the accuracy of machine analysis, the machine-vision importance map is introduced in BL; it adaptively utilizes the spatial and channel information from the deepest layer of VGG-net to guide local bit allocation. When the bit-rate is limited to 0.2 bpp, the average recognition accuracy (Top-1) of the BL of proposed method is 5.2%, 13.4%, 6.0%, and 7.4% higher than that of BPG, Webp, Mentzer, and NIC, respectively, on the ILSVRC2012 verification dataset. Meanwhile, the EL provides good visual experience.

Scalable image compression mechanism for surveillance video summary

Scalable image compression mechanism for surveillance video summary

Edge-aware coding tree unit hierarchical partitioning for quality scalable compression of depth maps

The scalable extension of the high efficiency video coding standard (SHVC) combines the large compression efficiency and high visual quality of HEVC, with the possibility of encoding different versions of the same video in a single bitstream. However, this comes at the cost of high computational complexity. In this context, many research works aim to reduce this complexity for texture images. We aim at achieving the same objective, but for depth maps whose characteristics make them different from conventional texture images. Depth maps are indeed characterized by areas of smoothly varying grey levels separated by sharp discontinuities at object boundaries. Preserving these discontinuities is crucial to enable high quality of synthesized views at the receiver side. In this paper, we propose a fast depth maps encoding scheme for quality scalable HEVC while exploiting depth maps characteristics, SHVC Coding Unit (CU) depth information and the correlation between the Base Layer (BL) and the Enhancement Layers (ELs) of SHVC. If a CU corresponds to a depth smooth region, we maintain the same best coding depth of its co-located in the BL. If a CU is a sharp region, the best coding depth is computed in the same way as in the original SHVC. Experiments are conducted and satisfying results are obtained as the proposed method improves the SHVC coding speed without a significant impact on the synthesized views Rate-Distortion tradeoff

Progressive hologram transmission using a view-dependent scalable compression scheme

Over the last few years, holography has been emerging as an alternative to stereoscopic imaging since it provides users with the most realistic and comfortable three-dimensional (3D) experience. However, high-quality holograms enabling a free-viewpoint visualization contain tremendous amount of data. Therefore, a user willing to access to a remote hologram repository would face high downloading time, even with high speed networks. To reduce transmission time, a joint viewpoint-quality scalable compression scheme is proposed. At the encoder side, the hologram is first decomposed into a sparse set of diffracted light rays using Matching Pursuit over a Gabor atoms dictionary. Then, the atoms corresponding to a given user’s viewpoint are selected to form a sub-hologram. Finally, the pruned atoms are sorted and encoded according to their importance for the reconstructed view. The proposed approach allows a progressive decoding of the sub-hologram from the first received atom. Streaming simulations for a moving user reveal that our approach outperforms conventional scalable codecs such as scalable H.265 and enables a practical streaming with a better quality of experience.

Zuckerli: A New Compressed Representation for Graphs

Zuckerli is a scalable compression system meant for large real-world graphs. Graphs are notoriously challenging structures to store efficiently due to their linked nature, which makes it hard to separate them into smaller, compact components. Therefore, effective compression is crucial when dealing with large graphs, which can have billions of nodes and edges. Furthermore, a good compression system should give the user fast and reasonably flexible access to parts of the compressed data without requiring full decompression, which may be unfeasible on their system. Compared to WebGraph, the de-facto standard in compressing real-world graphs, Zuckerli improves multiple aspects by using advanced compression techniques and novel heuristic graph algorithms. Zuckerli can produce both a compressed representation for storage and one which allows fast direct access to the adjacency lists of the compressed graph without decompressing the entire graph. We validate its effectiveness on real-world graphs with up to a billion nodes and 90 billion edges, conducting an extensive experimental evaluation of both compression density and decompression performance. We show that Zuckerli-compressed graphs are 10% to 29% smaller, and more than 20% in most cases, with a resource usage for decompression comparable to that of WebGraph.

Overview of Color Gamut Scalability

Displays’ new rendering capabilities combined with the ever-growing number of video applications have fueled the emergence of new video formats addressing wider color gamut and larger frame size. Thus, the need in scalable compression technology to provide backward compatibility with legacy devices and capitalize on the superior compression performance of High Efficiency Video Coding (HEVC) has increased significantly. This paper gives an overview of the work carried out in the Joint Collaborative Team on Video Coding of ITU-T Study Group 16 (VCEG) and ISO/IEC JTC1/SC29/WG11 motion picture experts group (MPEG) to define scalable extensions of HEVC (SHVC) targeting these market requirements. The color gamut scalability (CGS) tool of SHVC is specially designed to support efficient scalable coding with multiple layers in different color spaces. The genesis of the SHVC-CGS tool is presented and the performance of the various proposals is compared. Finally, the design of the recently adopted SHVC-CGS inter-layer prediction is detailed. The experimental results validate its efficiency in coding video with extended color gamut and high dynamic range.

A Scalable Data Chunk Similarity Based Compression Approach for Efficient Big Sensing Data Processing on Cloud

Big sensing data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity. Cloud computing provides a promising platform for big sensing data processing and storage as it provides a flexible stack of massive computing, storage, and software services in a scalable manner. Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big sensing data, traditional data compression techniques lack sufficient efficiency and scalability for data processing. Based on specific on-Cloud data compression requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic data units, the compression will be conducted over partitioned data chunks. To restore original data sets, some restoration functions and predictions will be designed. MapReduce is used for algorithm implementation to achieve extra scalability on Cloud. With real world meteorological big sensing data experiments on U-Cloud platform, we demonstrate that the proposed scalable compression approach based on data chunk similarity can significantly improve data compression efficiency with affordable data accuracy loss.

SIFT based video watermarking resistant to temporal scaling

In this paper, a blind video watermarking scheme is proposed which can resist temporal scaling such as frame dropping and frame rate adaptation due to scalable compression by exploiting the scale invariance property of the scale invariant feature transform (SIFT). A video scene can also be viewed from side plane where height is the number of rows in a video frame, width is the number of frames in the scene and depth is the number of columns in the frame. In this work, intensity values of selected embedding locations changed such that strong SIFT feature can be generated. SIFT features are extracted from side plane of the video. These newly generated SIFT features are used for watermark signal and are stored in the database for the authentication. A comprehensive set of experiments has been done to demonstrate the efficacy of the proposed scheme over the existing literature against temporal attacks.

Inter-Layer Prediction of Color in High Dynamic Range Image Scalable Compression.

This paper presents a color inter-layer prediction (ILP) method for scalable coding of high dynamic range (HDR) video content with a low dynamic range (LDR) base layer. Relying on the assumption of hue preservation between the colors of an HDR image and its LDR tone mapped version, we derived equations for predicting the chromatic components of the HDR layer given the decoded LDR layer. Two color representations are studied. In a first encoding scheme, the HDR image is represented in the classical Y'CbCr format. In addition, a second scheme is proposed using a colorspace based on the CIE u'v' uniform chromaticity scale diagram. In each case, different prediction equations are derived based on a color model ensuring the hue preservation. Our experiments highlight several advantages of using a CIE u'v'-based colorspace for the compression of HDR content, especially in a scalable context. In addition, our ILP scheme using this color representation improves on the state-of-the-art ILP method, which directly predicts the HDR layer u'v' components by computing the LDR layers u'v' values of each pixel.

QoE-Enabled Big Video Streaming for Large-Scale Heterogeneous Clients and Networks in Smart Cities

The rapid growth of the next-generation communication and networks is bringing video services into more pervasive environments. More and more users access and interact with video content using different devices, such as smart televisions, personal computers, tablets, smartphones, and wearable equipments. Providing heterogeneous Quality of Experience (QoE) that supports a wide variety of multimedia devices is critical to video broadcasting over the next-generation wireless network. This paper reviews practical video broadcasting technologies and examines current requirements ranging from heterogeneous devices to transmission technologies. Meanwhile, various coding methodologies, including QoE modeling, scalable compression efficiency, and flexible transmission, are also discussed. Moreover, this paper presents a typical paradigm as an example for video broadcasting with large-scale heterogeneity support, which enables QoE mapping, joint coding, flexible forward error coding, and cross-layer transmission, as well as optimal and dynamic adaptation to improve the overall receiving quality of heterogeneous devices. Finally, a brief summary of the key ideas and a discussion of interesting open areas are summarized at the end of this paper along with a future recommendation.

HIGHLY SECURE SCALABLE COMPRESSION OF ENCRYPTED IMAGES

A highly secure scalable compression method for stream cipher encrypted images is described in this journal. The input image first undergoes encryption and then shuffling. This shuffling in the image pixels enhances the security. For shuffling, Henon map is used. There are two layers for the scalable compression namely base layer and enhancement layer. Base layer bits are produced by coding a series of non-overlapping patches of uniformly down sampled version of encrypted image. In the enhancement layer pixels are selected by random permutation and then coded. From all the available pixel samples an iterative multi scale technique is used to reconstruct the image and finally performs decryption. The proposed method has high security.

Local inverse tone curve learning for high dynamic range image scalable compression.

This paper presents a scalable high dynamic range (HDR) image coding scheme in which the base layer is a low dynamic range version of the image that may have been generated by an arbitrary tone mapping operator (TMO). No restriction is imposed on the TMO, which can be either global or local, so as to fully respect the artistic intent of the producer. Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template-based interlayer prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for block-wise ILP. In addition, this paper shows that a linear adjustment of the initially predicted block can further improve the overall coding performance by using an efficient encoding scheme of the scaling parameters. Our experiments have shown an average bitrate saving of 47% on the HDR enhancement layer, compared with the previous local ILP methods.

Scalable Compression of Stream Cipher Encrypted Images Through Context-Adaptive Sampling

This paper proposes a novel scalable compression method for stream cipher encrypted images, where stream cipher is used in the standard format. The bit stream in the base layer is produced by coding a series of nonoverlapping patches of the uniformly down-sampled version of the encrypted image. An off-line learning approach can be exploited to model the reconstruction error from pixel samples of the original image patch, based on the intrinsic relationship between the local complexity and the length of the compressed bit stream. This error model leads to a greedy strategy of adaptively selecting pixels to be coded in the enhancement layer. At the decoder side, an iterative, multiscale technique is developed to reconstruct the image from all the available pixel samples. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-arts in terms of both rate-distortion performance and visual quality of the reconstructed images at low and medium rate regions.

Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering

Classical approaches for remote visualization and collaboration used in Computer-Aided Design and Engineering (CAD/E) applications are no longer appropriate due to the increasing amount of data generated, especially using standard networks. We introduce a lightweight and computing platform for scientific simulation, collaboration in engineering, 3D visualization and big data management. This ICT based platform provides scientists an “easy-to-integrate” generic tool, thus enabling worldwide collaboration and remote processing for any kind of data. The service-oriented architecture is based on the cloud computing paradigm and relies on standard internet technologies to be efficient on a large panel of networks and clients. In this paper, we discuss the need of innovations in (i) pre and post processing visualization services, (ii) 3D large scientific data set scalable compression and transmission methods, (iii) collaborative virtual environments, and (iv) collaboration in multi-domains of CAD/E. We propose our open platform for collaborative simulation and scientific big data analysis. This platform is now available as an open project with all core components licensed under LGPL V2.1. We provide two examples of usage of the platform in CAD/E for sustainability engineering from one academic application and one industrial case study. Firstly, we consider chemical process engineering showing the development of a domain specific service. With the rise of global warming issues and with growing importance granted to sustainable development, chemical process engineering has turned to think more and more environmentally. Indeed, the chemical engineer has now taken into account not only the engineering and economic criteria of the process, but also its environmental and social performances. Secondly, an example of natural hazards management illustrates the efficiency of our approach for remote collaboration that involves big data exchange and analysis between distant locations. Finally we underline the platform benefits and we open our platform through next activities in innovation techniques and inventive design.

Highly Scalable Compression Method for Super Resolution Multi Spectral Images

This paper provides a new technique for lossy multispectral images compression at very high data rates. Image compression is becoming more and more important, as new multispectral instruments are going to generate very high data rates due to the increased spatial and spectral resolutions. The Lossless compression does not provide a sufficient degree of data volume reduction to meet the bandwidth requirements of the downlink channel, so a lossy approach is the only possible solution. Multispectral data exhibits a large spectral correlation. Many techniques have been proposed for multispectral image data. Mainly in all the compression techniques to minimize the intra-band and inter-band correlation, some of them do in a single step and others do in a two-step transformation followed by an encoding scheme to eliminate the statistical redundancy or to give a desired bit-rate at low compression ratios based on the wavelet transform, where the spatial and the spectral redundancy are removed simultaneously. At higher compression ratios, a high quality wavelet based method in the spatial dimensions combined with the Improved Set Partitioning Hierarchical Trees (ISPHIT) in the spectral dimension gives the best results.

Scalable fragile watermarking for image authentication

Semi-fragile watermarks are used to detect unauthorised changes to an image, whereas tolerating allowed changes such as compression. Most semi-fragile algorithms that tolerate compression assume that because compression only removes the less visually significant data from an image, tampering with any data that would normally be removed by compression cannot affect a meaningful change to the image. Scalable compression allows a single compressed image to produce a variety of reduced resolution or reduced quality images, termed subimages, to suit the different display or bandwidth requirements of each user. However, highly scaled subimages remove a substantial fraction of the data in the original image, so the assumption used by most semi-fragile algorithms breaks down, as tampering with this data allows meaningful changes to the image content. The authors propose a scalable fragile watermarking algorithm for authentication of scalable JPEG2000 compressed images. It tolerates the loss of large amounts of image data because of resolution or quality scaling, producing no false alarms. Yet, it also protects that data from tampering, detecting even minor manipulations other than scaling, and is secure against mark transfer and collage attacks. Experimental results demonstrate this for scaling down to 1/1024th the area of the original or to 1/100th the file size.

A New Method of Scalable Image Compression Coding

This paper presents a new method of scalable image compression coding based on the wavelet transform. This method delimits the region of interest of the original image, and give a high-quality encoding to this region and a rough encoding to the rest. The result shows that in the limited memory space, this algorithm provides the coarser reconstruct image to satisfy the basal subject quality. Using this method, we can give a high quality encoding to the region for interest.

A Novel VLSI Architecture for SPHIT Encoder

In this Paper we propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. Our algorithm called highly scalable SPIHT (HS-SPIHT), supports spatial and SNR scalability and provides a bit stream that can be easily adapted (reordered) to given bandwidth and resolution requirements by a simple transcoder (parser). The HS-SPIHT algorithm adds the spatial scalability feature without sacrificing the SNR embeddedness property as found in the original SPIHT bit stream. HS-SPIHT finds applications in progressive Web browsing, flexible image storage and retrieval, and image transmission over heterogeneous networks. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.