Jimin Xiao

Depth Map Driven Hole Filling Algorithm Exploiting Temporal Correlation Information

The depth-image-based-rendering is a key technique to realize free viewpoint television. However, one critical problem in these systems is filling the disocclusion due to the 3-D warping process. This paper exploits the temporal correlation of texture and depth information to generate a background reference image. This is then used to fill the holes associated with the dynamic parts of the scene, whereas for static parts the traditional inpainting method is used. To generate the background reference image, the Gaussian mixture model is employed on the texture information, whereas, depth maps information are used to detect moving objects so as to enhance the background reference image. The proposed holes filling approach is particularly useful for the single-view-plus-depth format, where, contrary to the multi-view-plus-depth format, only information of one view could be used for this task. The experimental results show that objective and subjective gains can be achieved, and the gain ranges from 1 to 3 dB over the inpainting method.

One-class kernel subspace ensemble for medical image classification

Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

Dynamic Sub-GOP Forward Error Correction Code for Real-Time Video Applications

Reed-Solomon erasure codes are commonly studied as a method to protect the video streams when transmitted over unreliable networks. As a block-based error correcting code, on one hand, enlarging the block size can enhance the performance of the Reed-Solomon codes; on the other hand, large block size leads to long delay which is not tolerable for real-time video applications. In this paper a novel Dynamic Sub-GOP FEC (DSGF) approach is proposed to improve the performance of Reed-Solomon codes for video applications. With the proposed approach, the Sub-GOP, which contains more than one video frame, is dynamically tuned and used as the RS coding block, yet no delay is introduced. For a fixed number of extra introduced packets, for protection, the length of the Sub-GOP and the redundancy devoted to each Sub-GOP becomes a constrained optimization problem. To solve this problem, a fast greedy algorithm is proposed. Experimental results show that the proposed ap proach outperforms other real-time error resilient video coding technologies.

Real-Time Video Streaming Using Randomized Expanding Reed–Solomon Code

Forward error correction (FEC) codes are widely studied to protect streamed video over unreliable networks. Typically, enlarging the FEC coding block size can improve the error correction performance. For video streaming applications, this could be implemented by grouping more than one video frame into one FEC coding block. However, in this case, it leads to decoding delay, which is not tolerable for real-time video streaming applications. In this paper, to solve this dilemma, a real-time video streaming scheme using randomized expanding Reed-Solomon (RS) code is proposed. In this scheme, the RS coding block includes not only the video packets of the current frame, but could also include all the video packets of previous frames in the current group of pictures. At the decoding side, the parity-check equations of the current frame are jointly solved with all the parity-check equations of the previous frames. Since video packets of the following frames are not encompassed in the RS coding block, no delay will be caused for waiting for the video or parity packets of the following frames both at encoding and decoding sides. Experimental results show that the proposed scheme outperforms other real-time error resilient video streaming approaches significantly, specifically, for the Foreman sequence, the proposed scheme could provide 1.5 dB average gain over the state-of-the-art approach for 10% i.i.d. packet loss rate, whereas for the burst loss case, the average gain is more than 3 dB.MATLAB code of this paper is available for download at http://www.mmtlab.com.

Virtual-View-Assisted Video Super-Resolution and Enhancement

A 3-D multiview video gives users an experience that is different from that provided by a traditional video; however, it puts a huge burden on limited bandwidth resources. Mixed-resolution video in a multiview system can alleviate this problem by using different video resolutions for different views. However, to reduce visual uncomfortableness and to make this video format more suitable for free-viewpoint television, the low-resolution (LR) views need to be super-resolved to the target full resolution. In this paper, we propose a virtual-view-assisted super-resolution algorithm, where the inter-view similarity is used to determine whether the missing pixels in the super-resolved frame need to be filled by virtual-view pixels or by spatial interpolated pixels. The decision mechanism is steered by the texture characteristics of the neighbors of each missing pixel. Furthermore, the inter-view similarity is used, on the one hand, to enhance the quality of the virtual-view-copied pixels by compensating the luminance difference between different views and, on the other hand, to enhance the original LR pixels in the super-resolved frame by reducing their compression distortion. Thus, the proposed method can recover the details in regions with edges while maintaining good quality at smooth areas by properly exploiting the high-quality virtual-view pixels and the directional correlation of pixels. The experimental results demonstrate the effectiveness of the proposed approach with a peak signal-to-noise ratio gain of up to 3.85 dB.

Scalable Bit Allocation Between Texture and Depth Views for 3-D Video Streaming Over Heterogeneous Networks

In the multiview video plus depth (MVD) coding format, both texture and depth views are jointly compressed to represent the 3-D video content. The MVD format enables synthesis of virtual views through depth-image-based rendering; hence, distortion in the texture and depth views affects the quality of the synthesized virtual views. Bit allocation between texture and depth views has been studied with some promising results. However, to the best of our knowledge, most of the existing bit-allocation methods attempt to allocate a fixed amount of total bit rate between texture and depth views; that is, to select appropriate pair of quantization parameters for texture and depth views to maximize the synthesized view quality subject to a fixed total bit rate. In this paper we propose a scalable bit-allocation scheme, where a single ordering of texture and depth packets is derived and used to obtain optimal bit allocation between texture and depth views for any total target rates. In the proposed scheme, both texture and depth views are encoded using the quality scalable coding method; that is, medium grain scalable (MGS) coding of the Scalable Video Coding (SVC) extension of the Advanced Video Coding (H.264/AVC) standard. For varying target total bit rates, optimal bit truncation points for both texture and depth views can be obtained using the proposed scheme. Moreover, we propose to order the enhancement layer packets of the H.264/SVC MGS encoded depth view according to their contribution to the reduction of the synthesized view distortion. On one hand, this improves the depth view packet ordering when considered the rate-distortion performance of synthesized views, which is demonstrated by the experimental results. On the other hand, the information obtained in this step is used to facilitate optimal bit allocation between texture and depth views. Experimental results demonstrate the effectiveness of the proposed scalable bit-allocation scheme for texture and depth views.

A Real-Time Error Resilient Video Streaming Scheme Exploiting the Late- and Early-Arrival Packets

For real-time video streaming systems, the video packets arriving after the display deadline of their frames are considered as late-arrival packets, and typically they are discarded. This will affect the current frame and the following ones due to error propagations. For this reason, in this paper, we propose an approach to exploit the late-arrival and out-of-order packets, which includes two mechanisms. The first mechanism will use these packets to update the reference frames to make them more consistent with the encoder side, and this will eventually reduce the error propagations. The second mechanism will use these packets to increase the chance of successfully decoding the Reed-Solomon (RS) code. In the proposed approach, a sub-GOP based systematic RS code is used and optimized to exploit these packets, where the size of each sub-GOP and the parity packet number for each sub-GOP are optimally tuned, taking into consideration the maximum end-to-end delay, the network conditions, and other system parameters, so as to make the best use of the late-arrival packets and to exploit the out-of-order packets. Finally, the experimental results show the advantage of the proposed approach over other approaches.

Error-resilient video coding with end-to-end rate-distortion optimized at macroblock level

Intra macroblock refreshment is an effective approach for error-resilient video coding. In this paper, in addition to intra coding, we propose to add two macroblock coding modes to enhance the transmission robustness of the coded bitstream, which are inter coding with redundant macroblock and intra coding with redundant macroblock. The selection of coding modes and the parameters for coding the redundant version of the macroblock are determined by the rate-distortion optimization. It is worth mentioning that the end-to-end distortion is employed in the optimization procedure, which considers the channel conditions. Extensive simulation results show that the proposed approach outperforms other error-resilient approaches significantly; for some video sequences, the average PSNR can be up to 4 dB higher than that of the Optimal Intra Refreshment approach.

Macroblock Level Bits Allocation for Depth Maps in 3-D Video Coding

For 3-D videos, one commonly used representation method is texture videos plus depth maps for several selected viewpoints, whereas the other viewpoints are synthesized based on the available texture videos and depth maps with the depth-image-based rendering (DIBR) technique. As both the quality of the texture videos and depth maps will affect the quality of the synthesized views, bits allocation for the depth maps become indispensable. The existing bits allocation approaches are either inaccurate or requiring pre-encoding and analyzing in temporal dimension, making them unsuitable for the real-time applications. Motivated by the fact that different regions of the depth maps have different impacts on the synthesized image quality, a real-time macroblock level bits allocation approach is proposed, where different macroblocks of the depth maps are encoded with different quantization parameters and coding modes. As the bits allocation granularity is fine, the R-D performance of the proposed approach outperforms other bits allocation approaches significantly, while no additional pre-encoding delay is caused. Specifically, it can save more than 10% overall bit rate comparing with Morvan’s full search approach, while maintaining the same synthesized view quality.

Joint redundant motion vector and intra macroblock refreshment for video transmission

This paper proposes a scheme for error-resilient transmission of videos which jointly uses intra macroblock refreshment and redundant motion vector. The selection of using intra refreshment or redundant motion vector is determined by the rate-distortion optimization procedure. The end-to-end distortion is used for the rate-distortion optimization, which can be easily calculated with the recursive optimal per-pixel estimate (ROPE) method. Simulation results show that the proposed method outperforms both the intra refreshment approach and redundant motion vector approach significantly, when the two approaches are deployed separately. Specifically, for the Foreman sequence, the average PSNR of the proposed approach can be 1.12 dB higher than that of the intra refreshment approach and 5 dB higher than that of the redundant motion vector approach.