Anhong Wang

Wireless multicasting of video signals based on distributed compressed sensing

Abstract Multicasting of video signals over wireless networks has recently become a very popular application. Here, one major challenge is to accommodate heterogeneous users who have different channel characteristics and therefore will receive different noise-corrupted video packets of the same video source that is multicasted over the wireless network. This paper proposes a distributed compressed sensing based multicast scheme (DCS-cast), where a block-wise compressed sensing (BCS) is applied on video frames to obtain measurement data. The measurement data are then packed in an interleaved fashion and transmitted over OFDM channels. At the decoder side, users with different channel characteristics receive a certain number of packets and then reconstruct video frames by exploiting motion-based information. Due to the fact that the CS-measuring and interleaved packing together produce equally-important packets, users with good channel conditions will receive more packets so as to recover a better quality, which guarantees our DCS-cast scheme with a very graceful degradation rather than cliff effects. As compared to the benchmark SoftCast scheme, our DCS-cast is able to provide a better performance when some packets are lost during the transmission.

Multiple Description Video Coding Based on Human Visual System Characteristics

In this paper, a novel multiple description video coding scheme is proposed based on the characteristics of the human visual system (HVS). Due to the underlying spatial-temporal masking properties, human eyes cannot sense any changes below the just noticeable difference (JND) threshold. Therefore, at an encoder, only the visual information that cannot be predicted well within the JND tolerance needs to be encoded as redundant information, which leads to more effective redundancy allocation according to the HVS characteristics. Compared with the relevant existing schemes, the experimental results exhibit better performance of the proposed scheme at same bit rates, in terms of perceptual evaluation and subjective viewing.

A novel real-time and progressive secret image sharing with flexible shadows based on compressive sensing

A novel real-time and progressive secret image sharing scheme with flexible-size shadows is proposed. First, a secret image is measured by compressed sensing (CS). Then, the quantized measurement values are divided into n shadows using Shamirs (t,n)-threshold scheme. At the receiver side, the secret image can be reconstructed if any t of n shadows are obtained, but fewer than t shadows reveal no information. Due to the fact that CSs reconstruction quality is flexibly adaptive to the number of measurements, our scheme features flexible shadow size and obtains the property of real-time and progressive transmission as well as error resilient. Experimental results show that the proposed scheme achieves better performance in view of the reconstructed secret image.

Compressive Sensing for DCT Image

The Shannon/Nyquist sampling theorem claim that when capturing a signal, one must sample at least two times faster than the signal bandwidth in order to avoid losing information. Nowadays, compressive sensing, as a big idea in signal processing, is a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate. In this paper, compressive sensing is applied in DCT image. 1-D and 2-D DCT are adopted respectively and the corresponding schemes are designed to match the transform. Experimental results shows that for 2-D images, compressive sensing with 2-D DCT can achieve better performance than 1-D DCT whether in PSNR values or visual quality.

Distributed Multiple Description Coding

This book examines distributed video coding (DVC) and multiple description coding (MDC), two novel techniques designed to address the problems of conventional image and video compression coding. Covering all fundamental concepts and core technologies, the chapters can also be read as independent and self-sufficient, describing each methodology in sufficient detail to enable readers to repeat the corresponding experiments easily. Topics and features: provides a broad overview of DVC and MDC, from the basic principles to the latest research; covers sub-sampling based MDC, quantization based MDC, transform based MDC, and FEC based MDC; discusses Sleplian-Wolf coding based on Turbo and LDPC respectively, and comparing relative performance; includes original algorithms of MDC and DVC; presents the basic frameworks and experimental results, to help readers improve the efficiency of MDC and DVC; introduces the classical DVC system for mobile communications, providing the developmental environment in detail.

Reversible data hiding scheme based on histogram shifting of n-bit planes

In this paper, a reversible data hiding scheme is proposed based on histogram shifting of n-bit planes (nBPs). This scheme extracts nBPs from an 8-bit plane for each pixel to generate the bit plane truncation image (BPTI), and then block division is used in the BPTI. These operations can make the peak point of the block histogram more concentrated and improve the probability of the zero point in the block histogram. The histogram shifting method was used to embed secret bits into the peak point in each block. Note that this block was not utilized to embed secret bits if the zero point of a certain block did not exist, thus, there was no overflow or underflow in our scheme when the histogram was shifted. Our proposed scheme achieved higher hiding capacity than previous histogram-based schemes, and its visual quality was very satisfactory. The experimental results validated the expected merits of the proposed scheme.

An Improved Distributed Compressive Video Sensing Based on Adaptive Sparse Basis

This paper proposes an improved Distributed Compressive Video Sensing (DCVS) framework based on adaptive sparse basis, which integrates the recently emerging Distributed Video Coding (DVC) and Compressive Sensing (CS) theory. The proposed framework incorporates a low-complexity encoder and shifts most computation burden to the decoder-side. At the encoder, the video frames are sampled independently. However, the decoder recovers each block in a frame jointly using its side information (SI) and the state-of¨Cthe-art sparse basis generated by a few temporal neighboring blocks in previously reconstructed preceding and/or following key-frames. Experimental results show that the proposed framework outperforms not only the intra-encoding and intra-decoding scheme but also the DVCS scheme with wavelet transform based sparse basis.

Progressive image coding based on an adaptive block compressed sensing

This paper proposes an adaptive block-based compressed sensing (ABCS) technique to build a new progressive image coding scheme, in which both image acquisition and reconstruction are carried out in two layers. At the base layer, an original image is sampled and restored by the block-based compressed sensing (BCS) method with a low and fixed measurement rate. Second, all blocks in the enhancement layer are re-sampled with different rates according to a block classification. The final reconstruction of a block at the enhancement layer is performed in multiple stages where each stage only knows a part of sampled coefficients. We present some experimental results to show that our proposed ABCS method outperforms the BCS method; in particular, it produces a better visual quality in regions that contain edges, patterns, and textures.

Data hiding based on extended turtle shell matrix construction method

Data hiding research has focused mainly on determining how to embed secret data into various public host media, and to also ensure the host medium is not changed to a degree such that it can be perceived by the human eye. In 2014, Chang et al. proposed a novel concept, named the turtle shell matrix, to embed secret data. This scheme has obvious advantages with respect to its hiding capacity and image quality. However, its disadvantage is lack of flexibility due to the fixed turtle shell matrix structure. In this paper, we extend this turtle shell matrix structure into a different matrix model to meet different hiding capacity and image quality needs. Meanwhile, a general extraction function is derived to generate a matrix having a different turtle shell model. The values of the pixel pairs in the cover image are modified according to guidance provided by the turtle shell to hide a secret digit in an N-ary notational system. The experimental results show that the proposed scheme not only has better flexibility in balancing the trade-off between hiding capacity and stego-image quality, but also provides higher hiding capacity and stego-images with better visual quality than previous schemes.

Multiple Description Image Coding Based on DSC and Pixel Interleaving

This paper presents a novel multiple description image coding (MDC) using the theory of distributed source coding (DSC). The scheme is based on pixel interleaving MD image framework. A so-called Slepian-Wolf set partitioning in hierarchical tree (SW-SPIHT) is presented to efficiently and flexibly insert redundancy in each description and help to estimate the lost description. Experiments demonstrate that it can get better performance than some general MDC methods. Besides, it demonstrates higher robustness in packet-loss channel than pixel interleaving MDC method due to the error-correcting decoding adopted by DSC.