Progressive Transmission Scheme Research Articles

Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">successive refinement</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple descriptions</i> , respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> , an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.

Survey on some key technologies of virtual tourism system based on Web3D

&lt;p&gt;Some key technologies on how to build large-scale virtual tourism systssems comprehensively on Web browsers and mobiles were analyzed and the current R&amp;amp;D status on Web3D virtual tourism was surveyed insightfully. Then, some methods were summarized, including 3D trees or plants modeling, 3D architectural modeling, 3D Virtual Human behavior modeling, virtual agents path planning, collision detection and progressive transmission strategy suitable for developing large scale Web3D tourism scenarios. Also, some bottleneck problems of Web3D virtual tourism system were investigated. At the same time, the lightweight 3D engine, the lightweight 3D modeling, the lightweight 3D streaming and P2P based progressive transmission of huge Web3D tourism contents would become much helpful to breakthrough those bottlenecks of Web3D tourism systems were pointed out. In addition, all kinds of Web3D engines in terms of lightweight, realism and efficiency that would be a good reference for developers to choose during various applications were compared comprehensively. Finally, the prospect of future investigation of Web3D tourism system is presented, which will be going on in terms of four characteristics lightweight, high-speed, realism, beauty. &lt;/p&gt;

A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images.

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

A Three-Stage Progressive Transmission Scheme for Virtual Environments Over Lossy Networks

The rapid development of Virtual Environments (VEs) gives rise to its prominent performance in a myriad of multimedia applications. Accompanied by this is the increasingly massive data used for ascending user's visual experience. Recently, 3D streaming makes it viable to put VEs into real-time use through progressive transmission. Nonetheless, downloading VEs over inevitable lossy networks still remains a major bottleneck that urgently needs to be solved. In this paper, we propose a three-stage progressive transmission scheme for VEs over lossy networks, which aims to minimize distortion even when encountering poor network condition. The first stage is the Pre-coding Stage that offers the most feasible transmission scheme for subsequent transmission, in this stage we combine the advanced interest management algorithm and our proposed scene distortion estimation algorithm to bring up a scene update strategy which takes the user's visual features, quality of the scene and bandwidth estimation into account, so as to determine the download priority comprehensively. In the following Transmitting Stage, we apply Transmission Control Protocol (TCP) to make sure that we convey the essential information. More importantly, we adopt expanding window fountain (EWF) codes to provide an unequal error protection (UEP) for data with different importance. Eventually in the third After-process Stage, we employ a geometry prediction algorithm for the scenario with packet loss. The final results of our experiments show that the three-stage scheme can offer preeminent visual experience especially when it comes to a relatively inferior network condition.

Multi-level sample importance ranking based progressive transmission strategy for time series body sensor data

Body sensors have gained increasing interest during the past several years. With more applications deployed, it is imperative to ensure the success of data analysis, which largely depends on data transmission reliability as well as the importance of samples received. Traditional approaches focus on improving data reliability through various schemes such as the prioritization of MAC access. However, in this paper we analyze the characteristics of time series body sensor data and propose to rank sample importance based on a multi-level approach. With this approach, samples are grouped into five levels, indicating their importance with regard to data analysis. Then, a progressive transmission strategy is designed to transmit samples in order of their importance so that the overall received data quality is maximized. Extensive experiment results indicate that as much as 40–60% bandwidth saving can be achieved while meeting the requirements of data analysis algorithms.

A progressive transmission strategy for GIS vector data under the precondition of pixel losslessness

It is well known that the final results of geographic information system (GIS) mapping are composed of rasterized pixels in most display devices. Based on this, pixel equivalence is proposed as two vector data that can produce the same raster image on a device under the same mapping processes and parameters. Moreover, if between the two data, one is considered the original data and the other is its reduced-quantity version, then the latter can be regarded as pixel losslessness relative to the former. In this case, the reduced-quantity version can be used instead of the original data to produce the same result from the perspective of visualization. The aim of this article is to explore how to produce the transmitted data, including an initial code and a series of increments that are used for progressive transmission, to retain pixel losslessness. By illustrating that the vector data can be represented by a vertex or ordered sequence of vertices, this article introduces the detailed concepts of pixel equivalence and pixel losslessness of vector data. Then, the requirements for pixel equivalence between two vertices are deduced by analyzing the coordinate transform processes under specific assumptions. The method to generate an initial code and a series of increments related to a single vertex is proposed to produce vertices that are pixel equivalence to the original vertex under different coordinate transform processes. By discussing the traversing mode of the ordered sequence of vertices based on quadtree cells, a coding algorithm is proposed to develop the progressive transmission related to the multi-vertex and to generate reduced-quantity versions to retain pixel losslessness. Finally, experiments are designed to demonstrate that, under the precondition of pixel losslessness, the proposed progressive transmission method can significantly reduce the amount of transmitted data in the network environment.

Progressive transmission based on wavelet used in mobile visual search

Mobile visual search has become a hot topic in both academia and business circles. A typical MVS system uses the camera phone to initiate search queries, following a client-server architecture. Transmission overload turns out to be a bottleneck. An effective way to reduce this is exploiting progressive transmission strategy. However, the acceleration effects of existing methods are limited because they only decrease the amount of descriptors delivered. To compress the descriptors, many methods have been offered. One problem is that they cannot restore the original descriptor and the more compressive the descriptor is, the lower the precision is. To conquer these problems, we propose a progressive transmission strategy based on wavelet theorem, which can both compress the descriptor and restore the original data. Extensive experiments have been done on Stanford database, demonstrating that our strategy outperforms other strategies, when delivering the same amount of data.

Progressive Transmission of Vector Spatial Data

Spatial Information has special format, diverse storage and large amount of data volume, together with the existing limitation of the network bandwidth, all those above caused some barriers to its transfer application. This paper focuses on a progressive transmission scheme for vector spatial data, it consists of three parts: hierarchical simplification of vector spatial data based on improved genetic algorithm, a multi-level incremental storage model in accordance with the fixed scale progressive transmission, data extraction and data restructuring by traversing the incremental data. At last, the paper tests the algorithm of the hierarchical simplification and the response time in client side, the results prove that the method we proposed are useable.

A model for progressive transmission of spatial data based on shape complexity

Due to the limited bandwidth available to mobile devices transmitting large amount of geographic data over the Internet to these devices is challenging. Such data is often high-resolution vector data and is far too detailed with respect to most location-based services (LBS) user requirements. A less detailed version may be sent prior to the complete dataset using a progressive transmission strategy. Progressive transmission is generally performed by transmitting a series of independent pre-computed representations of the original dataset at increasing levels of detail where the transitions between these levels are not necessarily smooth. A model is proposed in this paper for selective progressive transmission which will provide smoother transmission over increasing levels of detail. We define criteria for the comparison of similarity between the progressive states of the vector-data based on shape complexity of the polygon features. This allows development of a real-time strategy for the progressive transmission of vector data over the Internet to mobile devices.

ROI-based procedures for progressive transmission of digital images: A comparison

Nowadays, problems arise when handling large-sized images (i.e. medical image such as Computed Tomographies or satellite images) of 10, 50, 100 or more Megabytes, due to the amount of time required for transmitting and displaying, this time being even worse when a narrow bandwidth transmission medium is involved (i.e. dial-up or mobile network), because the receiver must wait until the entire image has arrived. To solve this issue, progressive transmission schemes are used. These schemes allow the image sender to encode the image data in such a way that it is possible for the receiver to perform a reconstruction of the original image from the very beginning of transmission. Despite this reconstruction being, of course, partial, it is possible to improve the reconstruction on the fly, as more and more data of the original image are received. There are many progressive transmission methods available, such as it planes, TSVQ, DPCM, and, more recently, matrix polynomial interpolation, Discrete Cosine Transform (DCT, used in JPEG) and wavelets (used in JPEG 2000). However, none of them is well suited, or perform poorly, when, in addition to progressive transmission, we want to include also ROIs (Region Of Interest) handling. In the progressive transmission of ROIs, we want not only to reconstruct the image as we receive image data, but also to be able to select which part or parts of the emerging image we think are relevant and want to receive first, and which part or parts are of no interest. In this context we present an algorithm for lossy adaptive encoding based on singular value decomposition (SVD). This algorithm turns out to be well suited for progressive transmission and ROI selection of 2D and 3D images, as it is able to avoid redundancy in data transmission and does not require any sort of data recodification, even if we select arbitrary ROIs on the fly. We compare the performing of SVD with DCT and wavelets and show the results.

Turbo Codes-Based Image Transmission for Channels With Multiple Types of Distortion

Product codes are generally used for progressive image transmission when random errors and packet loss (or burst errors) co-exist. However, the optimal rate allocation considering both component codes gives rise to high-optimization complexity. In addition, the decoding performance may be degraded quickly when the channel varies beyond the design point. In this paper, we propose a new unequal error protection (UEP) scheme for progressive image transmission by using rate-compatible punctured Turbo codes (RCPT) and cyclic redundancy check (CRC) codes only. By sophisticatedly interleaving each coded frame, the packet loss can be converted into randomly punctured bits in a Turbo code. Therefore, error control in noisy channels with different types of errors is equivalent to dealing with random bit errors only, with reduced turbo code rates. A genetic algorithm-based method is presented to further reduce the optimization complexity. This proposed method not only gives a better performance than product codes in given channel conditions but is also more robust to the channel variation. Finally, to break down the error floor of turbo decoding, we further extend the above RCPT/CRC protection to a product code scheme by adding a Reed-Solomon (RS) code across the frames. The associated rate allocation is discussed and further improvement is demonstrated.

Extended Multiresolution Lossless Video Coding Using In-Band Spatio-Temporal Prediction

Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper combines a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet transform domain to create multiresolution lossless video coding. Based on the image statistics of the wavelet transform domains in successive frames, inter/intra frame adaptive prediction is applied to the appropriate wavelet transform domain. This adaptation offers superior compression performance. A progressive transmission scheme is also proposed for effective resolution scalability. Experiments on test sequences confirm the effectiveness of the proposed algorithm.

Lossless compression-based progressive image transmission scheme

The lossless compression of images is widely used in medical imaging and remote sensing applications. Also, progressive transmission of images is often desirable because it can reduce the transmission bits of an image. Therefore, combining the features of lossless compression and progressive transmission of images has been intensely researched. The bitplane method (BPM) is the simplest way to implement a lossless progressive image transmission system. In the present paper, a new block-based scheme for lossless progressive image transmission is proposed. This scheme will reduce the transmission load and improve the image quality of BPM. This method first performs a quantization operation upon the blocks of an image. Next, these blocks are encoded with fewer bits, and the bits are then transmitted phase by phase. The experimental results show that the image quality of this method is better than those in the BPM and improved BPM in related traditional works under the same transmission load. Moreover, during the first phase, the difference in peak signal-to-noise ratio between the present method and BPM is exactly equal up to 8.85 dB. This method is therefore effective for lossless progressive image transmission.

The relationship between information prioritization and visual distinctness in two progressive image transmission schemes

The goal of this paper is to analyze the relation between the visual distinctness and the priority assigned to the information in two different progressive transmission schemes: the Set Partitioning in Hierarchical Trees (SPIHT) and the Rational Embedded Wavelet Image Coding (REWIC). SPIHT assigns priority based on the mean squared-error (MSE) distortion measure. REWIC quantifies the visual distinctness through the expected increase in utility per coding bit, using a simple function of the Kullback–Leibler's information gain, called dist REWIC . We will show that in progressive transmission, the information selected through the increase in utility per coding bit corresponds to information with the largest visual relevance to a higher degree than the information selected on the basis of the MSE metric.

Rate control optimization in embedded wavelet coding

To circumvent the lack of knowledge of what measure is more suitable for optimization of the trade-off between image fidelity and coding rate, this paper shall introduce a novel mathematical methodology for rate control by organizing the progressive transmission in accordance with coherence constraints for avoiding forms of behavioral inconsistency. This methodology could be used within a progressive transmission scheme to produce a new compression method (a rational embedded wavelet image coding). Here we compare a new coding scheme based on the proposed rate control technique against the state of art coder in progressive transmission and demonstrate its superior performance based on an objective criterion of evaluation.

Rational systems exhibit moderate risk aversion with respect to “gambles” on variable-resolution compression

In an embedded wavelet scheme for progressive transmission, a tree structure naturally defines the spatial relationship on the hierarchical pyramid. Transform coefficients over each tree correspond to a unique local spatial region of the original image, and they can be coded bit-plane by bit-plane through successive-approximation quantization. After receiving the approximate value of some coefficients, the decoder can obtain a reconstructed image. We show a rational system for progressive transmission that, in absence of a priori knowledge about regions of interest, chooses at any truncation time among alternative trees for further transmission in such a way as to avoid certain forms of behavioral inconsistency. We prove that some rational transmission systems might exhibit aversion to risk involving gambles on tree-dependent quality of encoding while others favor taking such risks. Based on an acceptable predictor for visual distinctness from digital imagery, we demonstrate that, without any outside knowledge, risk-prone systems as well as those with strong risk aversion appear in capable of attaining the quality of reconstructions that can be achieved with moderate risk-averse behavior.

3D mesh geometry filtering algorithms for progressive transmission schemes

A number of static and multi-resolution methods have been introduced in recent years to compress 3D meshes. In most of these methods, the connectivity information is encoded without loss of information, but user-controllable loss of information is tolerated while compressing the geometry and property data. All these methods are very efficient at compressing the connectivity information, in some cases to a fraction of a bit per vertex, but the geometry and property data typically occupies much more room in the compressed bitstream than the compressed connectivity data. In this paper, we investigate the use of polynomial linear filtering as studied in the Refs. [Taubin G. A signal processing approach to fair surface design. Computer Graphics Proc., Annual Conference Series 1995;351–358; Taubin G, Zhang T, Golub G. Optimal surface smoothing as filter design. IBM Research report RC–20,404, 1996], as a global predictor for the geometry data of a 3D mesh in multi-resolution 3D geometry compression schemes. Rather than introducing a new method to encode the multi-resolution connectivity information, we choose one of the efficient existing schemes depending on the structure of the multi-resolution data. After encoding the geometry of the lowest level of detail with an existing scheme, the geometry of each subsequent level of detail is predicted by applying a polynomial filter to the geometry of its predecessor lifted to the connectivity of the current level. The polynomial filter is designed to minimize the l2-norm of the approximation error but other norms can be used as well. Three properties of the filtered mesh are studied next: accuracy, robustness and compression ratio. The Zeroth Order Filter (unit polynomial) is found to have the best compression ratio. But higher order filters achieve better accuracy and robustness properties at the price of a slight decrease of the compression ratio.

Wavelet-based medical image compression

In view of the increasingly important role played by digital medical imaging in modern health care and the consequent blow up in the amount of image data that have to be economically stored and/or transmitted, the need for the development of image compression systems that combine high compression performance and preservation of critical information is ever growing. A powerful compression scheme that is based on the state-of-the-art in wavelet-based compression is presented in this paper. Compression is achieved via efficient encoding of wavelet zerotrees (with the embedded zerotree wavelet (EZW) algorithm) and subsequent entropy coding. The performance of the basic version of EZW is improved upon by a simple, yet effective, way of a more accurate estimation of the centroids of the quantization intervals, at a negligible cost in side information. Regarding the entropy coding stage, a novel RLE-based coder is proposed that proves to be much simpler and faster yet only slightly worse than context-dependent adaptive arithmetic coding. A useful and flexible compromise between the need for high compression and the requirement for preservation of selected regions of interest is provided through two intelligent, yet simple, ways of achieving the so-called selective compression. The use of the lifting scheme in achieving compression that is guaranteed to be lossless in the presence of numerical inaccuracies is being investigated with interesting preliminary results. Experimental results are presented that verify the superiority of our scheme over conventional block transform coding techniques (JPEG) with respect to both objective and subjective criteria. The high potential of our scheme for progressive transmission, where the regions of interest are given the highest priority, is also demonstrated.

A low bit rate hybrid coding scheme for progressive image transmission

In this paper, a hybrid coding scheme for progressive image transmission (PIT) based on two components, that is the smooth and edge components, is presented. The image is first decimated by using the pyramid coding technique with a decimation factor of 4:1. A multistage predictive locally adaptive vector quantization (MSPVQ) progressively transmits the decimated image that represents the smooth component of the scene. The first stage of the proposed (VQ) gives a general grasp of the Lena image at a compression ratio as high as 80:1. An adaptive VQ (LAVQ) encodes the difference image, which contains the quantization and edge error of the smooth component. By adding the encoded image components, an excellent reproduction is achieved at a low bit rate of only 0.2 bit per pixel (bpp), i.e. compression ratio of 40:1, with peak signal-to-noise ratio (SNR) of 36.92 dB. It is also shown that, for the Lena image and for the same PSNR of about 34 dB, the bit rate using the proposed algorithm is very low; only 0.2 bpp. This bit rate is lower than about 0.6 bpp using the JPEG algorithm, and much lower than that using BTC-DCT or SB-DCT algorithms. Thus the proposed algorithm results in a saving of about 0.4 in the bit rate for the same visual quality.

Skeleton-based morphological coding of binary images

This paper presents new properties of the discrete morphological skeleton representation of binary images, along with a novel coding scheme for lossless binary image compression that is based on these properties. Following a short review of the theoretical background, two sets of new properties of the discrete morphological skeleton representation of binary images are proved. The first one leads to the conclusion that only the radii of skeleton points belonging to a subset of the ultimate erosions are needed for perfect reconstruction. This corresponds to a lossless sampling of the quench function. The second set of new properties is related to deterministic prediction of skeletonal information in a progressive transmission scheme. Based on the new properties, a novel coding scheme for binary images is presented. The proposed scheme is suitable for progressive transmission and fast implementation. Computer simulations, also presented, show that the proposed coding scheme substantially improves the results obtained by previous skeleton-based coders, and performs better than classical coders, including run-length/Huffman, quadtree, and chain coders. For facsimile images, its performance can be placed between the modified read (MR) method (K=4) and modified modified read (MMR) method.