Unreliable communication channels might lead to packet losses and bit errors in the videos transmitted through it, which will cause severe video quality degradation. This is even worse for HEVC since more advanced and powerful motion estimation methods are introduced to further remove the inter-frame dependency and thus improve the coding efficiency. Once a Motion Vector (MV) is lost or corrupted, it will cause distortion in the decoded frame. More importantly, due to motion compensation, the error will propagate along the motion prediction path, accumulate over time, and significantly degrade the overall video presentation quality. To address this problem, we study the problem of encoder-sider error resilient coding for HEVC and propose a constrained motion estimation scheme to mitigate the problem of error propagation to subsequent frames. The approach is achieved by cutting off MV dependencies and limiting the block regions which are predicted by temporal motion vector. The experimental results show that the proposed method can effectively suppress the error propagation caused by bit errors of motion vector and can improve the robustness of the stream in the bit error channels. When the bit error probability is 10<sup>-5</sup>, an increase of the decoded video quality (PSNR) by up to1.310dB and on average 0.762 dB can be achieved, compared to the reference HEVC.
As remote sensing image applications are often characterized with limited bandwidth and high-quality demands, higher coding performance of remote sensing images are desirable. The embedded block coding with optimal truncation (EBCOT) is the fundamental part of JPEG2000 image compression standard. However, EBCOT only considers correlation within a sub-band and utilizes a context template of eight spatially neighboring coefficients in prediction. The existing optimization methods in literature using the current context template prove little performance improvements. To address this problem, this paper presents a new mutual information (MI)-based context template selection and modeling method. By further considering the correlation across the sub-bands, the potential prediction coefficients, including neighbors, far neighbors, parent and parent neighbors, are comprehensively examined and selected in such a manner that achieves a nice trade-off between the MI-based correlation criterion and the prediction complexity. Based on the selected context template, a high-order prediction model, which jointly considers the weight and the significance state of each coefficient, is proposed. Experimental results show that the proposed algorithm consistently outperforms the benchmark JPEG2000 standard and state-of-the-art algorithms in term of coding efficiency at a competitive computational cost, which makes it desirable in real-time compression applications, especially for remote sensing images.
The measurement of visual quality is of fundamental importance to remote sensing image compression, especially for image quality assessment and compression algorithm optimization. We exploit the distortion features of optical remote sensing image compression and propose a full-reference image quality metric based on multilevel distortions (MLD), which assesses image quality by calculating distortions of three levels (such as pixel-level, contexture-level, and content-level) between original images and compressed images. Based on this, a multiscale MLD (MMLD) algorithm is designed and it outperforms the other current methods in our testing. In order to validate the performance of our algorithm, a special remote sensing image compression distortion (RICD) database is constructed, involving 250 remote sensing images compressed with different algorithms and various distortions. Experimental results on RICD and Laboratory for Image and Video Engineering databases show that the proposed MMLD algorithm has better consistency with subjective perception values than current state-of-the-art methods in remote sensing image compression assessment, and the objective assessment results can show the distortion features and visual quality of compressed image well. It is suitable to be the evaluation criteria for optical remote sensing image compression.
Packet scheduling is of great importance to the performance optimization of multi-session video streaming over
wireless mesh networks. In this paper, we explore the time-varying characteristics of the input videos and propose a
Content-and-Deadline-Aware Scheduling (CDAS) scheme for multi-session video streaming over wireless multi-hop
networks. The basic idea of proposed scheduling scheme is to choose and transmit more packets with higher importance
while meeting their stringent delay constraints, to maximize the constructed video quality at the receivers' side. More
specifically, the packet schedule is determined in such a way that not only the backlog, but also the contributions to the
reconstructed videos as well as the stringent delay constraints and time-varying network condition are all considered.
This is enabled by our proposed priority model composed of two major components: content priority and scheduling
priority. For the content priority, we develop a fast and efficient packet-level transmission distortion model to accurately
predict the corresponding distortion for a lost packet at the encoder side. For scheduling priority, we consider delay
requirement and dynamic network conditions of subsequent transmission links. Our extensive simulations demonstrate
that the proposed transmission distortion model and the CDAS policy significantly improve the performance of
multi-session video streaming over wireless mesh networks.
In this paper, a SGNN (Self-Generating Neural Network)-based method is applied to image segmentation, which is
implemented automatically by autonomously clustering the pixels according to their gray values. The optimization of
SGNN is studied to further improve the accuracy and robustness, as well as to reduce the computational complexity of the
segmentation. The experimental results show that the optimized SGNN gets better segmentation results and outperforms
the existing methods for its distinguished advantages of perfect segmentation without any manual intervention, high
self-learning capacity, less computational complexity, robustness to noise, etc. What's more, the experimental results
suggest that the proposed method can be widely used in segmentation of all typical images, such as IR (Infrared) images,
visible images, X-ray images, and MR (Magnetic Resonance) Images.
Conference Committee Involvement (3)
2013 IEEE Global Communications Conference-Communications Software, Services and Multimedia Symposium
9 December 2013 |
2nd IEEE International Workshop on Engineering Mobile-Based Software and Networking Applications (EMOBS 2009) IN CONJUNCTION WITH COMPSAC 2009
21 July 2009 |
International Symposium on Multimedia over Wireless (ISMW) 2008