Huihui Bai

Optimized Multiple Description Lattice Vector Quantization for Wavelet Image Coding

Multiple description (MD) coding is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. In this paper, an effective MD image coding scheme is introduced based on the MD lattice vector quantization (MDLVQ) for the wavelet transformed images. In view of the characteristics of wavelet coefficients in different frequency subbands, MDLVQ is applied in an optimized way, including an appropriate construction of wavelet coefficient vectors, the optimization of MDLVQ encoding parameters such as the choice of sublattice index values and the quantization accuracy for different subbands. More importantly, optimized side decoding is employed to predict lost information based on inter-vector correlation and an alternative transmission way for further reducing side distortion. Experimental results validate the effectiveness of the proposed scheme with better performance than some other tested MD image codecs including that based on optimized MD scalar quantization.

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.

A fast depth-map wedgelet partitioning scheme for intra prediction in 3D video coding

By employing 35 intra prediction modes, HEVC standard performs better to remove spatial redundancy between the current block and its neighbors. Although 3D video coding has adopted HEVC intra prediction and Depth Modeling Modes (DMM) technology to improve the performance, the Explicit Wedgelet Partition Mode within DMM brings unaffordable complexity to compress depth map. Based on the way of obtaining the picture texture from the mode with Sum of Absolute Transform Difference in rough mode decision, we propose a fast scheme to determine Wedgelet Partition in intra prediction, which leads to significant computational saving with marginal BD-rate increase after decoder-side view synthesis.

Control-Point Representation and Differential Coding Affine-Motion Compensation

The affine-motion model is able to capture rotation, zooming, and the deformation of moving objects, thereby providing a better motion-compensated prediction. However, it is not widely used due to difficulty in both estimation and efficient coding of its motion parameters. To alleviate this problem, a new control-point representation that favors differential coding is proposed for efficient compression of affine parameters. By exploiting the spatial correlation between adjacent coding blocks, motion vectors at control points can be predicted and thus efficiently coded, leading to overall improved performance. To evaluate the proposed method, four new affine prediction modes are designed and embedded into the high-efficiency video coding test model HM1.0. The encoder adaptively chooses whether to use the new affine mode in an operational rate-distortion optimization. Bitrate savings up to 33.82% in low-delay and 23.90% in random-access test conditions are obtained for low-complexity encoder settings. For high-efficiency settings, bitrate savings up to 14.26% and 4.89% for these two modes are observed.

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.

Optimized multiple description image coding using lattice vector quantization

Multiple description (MD) coding has many applications in the transmission of images over unreliable packet or multiple path networks that cannot guarantee lossless data delivery. In this paper, an effective multiple description image coding scheme is introduced. This scheme is mainly based on the wavelet transform and multiple description lattice vector quantization (MDLVQ). The characteristics of wavelet coefficients in different frequency subbands are taken into account in the design of the MD image coder with different optimized MDLVQ parameters. The experimental results are presented to demonstrate the effectiveness of the proposed scheme.

Fast bottom-up pruning for HEVC intraframe coding

In intraframe coding of the High Efficiency Video Coding (HEVC) standard, up to 35 modes are defined for intra prediction and the quadtree structure is used for adaptive block partition. While such flexibility leads to more efficient compression, it also dramatically increases the encoder complexity. In this paper, a simple yet effective fast bottom-up pruning algorithm is proposed to reduce the computational cost. Mode decision at a large coding unit (CU) is selectively skipped based on the block structures of its sub-CUs. Our experimental results show that the proposed scheme can effectively reduce the encoder complexity without compromising the compression efficiency.

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.

Robust multiple description distributed video coding using optimized zero-padding

Distributed video coding (DVC) arouses high interests due to its property of low-complexity encoding. This paper proposes a robust multiple description DVC (MDDVC) under the constraint of low-complexity encoding. In MDDVC, zeros are padded to each frame and the resulting big-size video is divided into multiple descriptions. Then, each description is compressed by a hybrid DVC (HDVC) codec and transmitted over different channel. When one channel does not work, the lost HDVC description is estimated by the received from other channel, which guarantees the robustness of the system; MDDVC moves the complex motion estimation totally to the decoder so it features low-complexity encoding. In the pre-processing, an optimized zero-padding is also proposed to improve the performance. Experimental results exhibit that the proposed MDDVC scheme achieves better rate-distortion performance and robustness than the referenced especially when packet-loss rate is high.

View synthesis based on background update with gaussian mixture model

View Synthesis is a key technique for 3-D video and free view video generation. In the traditional 3-D video and free view video, there have to be many real cameras to capture the scene at the large cost. With the help of the view synthesis technique, a limited number of cameras can achieve the goal of multi-view generation. However, some holes will appear in the synthesized views due to the 3-D warping process in the view synthesis system. These holes seriously affect the quality of the synthesized images, especially for the disocclusions which is caused from the occluded regions in the original view may become visible in the virtual view. In this paper, we focus on the disocclustion filling after 3-D warping in view synthesis system. An approach is proposed to fill the discocclusion by using the real background information covered in the original view, which is based on the observation that the covered information in the current frame may be visible in the next frames of the same view. In this approach, the stable texture and depth background reference frames are generated for the left and right view, respectively, which are based on the Gaussian Mixture Model (GMM). Then, in the view synthesis system, a stable background reference frame is merged by the left and right warped images with the corresponding texture and depth background reference frames. Finally, the merged frame is used to fill the disocclusion regions of each merged frame as the background reference frame. The experimental results show that the proposed scheme can achieve better objective quality, especially for the scene with moving objects.