Wenpeng Ding

Screen Content Coding Based on HEVC Framework

Screen content like cartoons, captures of typical computer screens or video with text overlay or news ticker is an important category of video, which needs new techniques beyond the existing video coding techniques. In this paper, we analyze the characteristics of screen content and coding efficiency of HEVC on screen content. We propose a new coding scheme, which adopts a non-transform representation, separating screen content into color component and structure component. Based on the proposed representation, two coding modes are designed for screen content to exploit the directional correlation and non-translational changes in screen video sequences. The proposed scheme is then seamlessly incorporated into the HEVC structure and implemented into HEVC range extension reference software HM9.0. Experimental results show that the proposed scheme achieves up to 52.6% bitrate saving compared with HM9.0. On average, 35.1%, 29.2% and 23.6% bitrate saving are achieved with intra, random-access and low-delay configurations, respectively. The visual quality of the decoded video sequences is also significantly improved by reducing ringing artifacts around sharp edges and reserving the shape of text without blur.

Hash-Based Block Matching for Screen Content Coding

By considering the increasing importance of screen contents, the high efficiency video coding (HEVC) standard includes screen content coding as one of its requirements. In this paper, we demonstrate that enabling frame level block searching in HEVC can significantly improve coding efficiency on screen contents. We propose a hash-based block matching scheme for the intra block copy mode and the motion estimation process, which enables frame level block searching in HEVC without changing the HEVC syntaxes. In the proposed scheme, the blocks sharing the same hash values with the current block are selected as prediction candidates. Then the hash-based block selection is employed to select the best candidates. To achieve the best coding efficiency, the rate distortion optimization is further employed to improve the proposed scheme by balancing the coding cost of motion vectors and prediction difference. Compared with HEVC, the proposed scheme achieves 21% and 37% bitrate saving with all intra and low delay configurations with encoding time reduction. Up to 59% bitrate saving can be achieved on sequences with large motions.

2-D Dictionary Based Video Coding for Screen Contents

Screen contents with complex structure contain random combination of texts, graphics and camera-captured images, which makes them difficult to be compressed efficiently by traditional video codecs. In this paper, we propose a 2-D dictionary based scheme to exploit the repeated patterns on screen content. In the proposed scheme, the current block is predicted from the reconstructed region using a hash-based block searching scheme. A hierarchical two-level hash based searching scheme is designed to find the best matching block for each block. The first-level hash function is used to search the blocks similar to the current block in the constructed 2-D dictionary. The second-level hash function is used to update the 2-D dictionary, which filters out the identical blocks from the blocks found using the first-level hash function. The proposed scheme is incorporated into HEVC framework as an additional mode. Experimental results show that the proposed scheme achieves significantly coding performance improvements on screen contents compared with HEVC.

Two dimensional analysis sparse model

An analysis sparse model represents an image signal by multiplying it using an analysis dictionary, leading to a sparse outcome. It transforms an image (two dimensional signal) into a one-dimensional (1D) vector. However, this 1D model ignores the two dimensional property and breaks the local spatial correlation inside images. In this paper, we propose a two dimensional (2D) analysis sparse model. Our 2D model uses two analysis dictionaries to efficiently exploit the horizontal and vertical features simultaneously. The corresponding sparse coding and dictionary learning algorithm are also presented in this paper. The 2D sparse model is further evaluated for image denoising. Experimental results demonstrate our 2D analysis sparse model outperforms a state-of-the-art 1D analysis model in terms of both denoising ability and memory usage.

Compound image compression by multi-stage prediction

Computer generated compound images contain not only photographic images but also text and graphics images, which makes it difficult to be efficiently compressed using exiting image and video coding standards. This paper presents a new compound image coding scheme based on the intra coding framework of the upcoming high efficiency video coding (HEVC) standard. The proposed scheme first decomposes the compound image into color components and structure components. Then two-stage prediction scheme is employed to exploit the correlation among structure components, where the first prediction is generated by directional prediction and second prediction is generated by template matching. Experimental results show that the proposed scheme achieves up to 10.2 dB coding gain on compound images compared with HEVC. And on average it achieves 42.2 percent bitrate saving compared with HEVC.

A low-rank matrix completion based intra prediction for H.264/AVC

Intra prediction plays an important role in reducing the spatial redundancy for intra frame encoding in H.264/AVC. In this paper, we propose a low-rank matrix completion based intra prediction to improve the prediction efficiency. According to the low-rank matrix completion theory, a low-rank matrix can be exactly recovered from quite limited samples with high probability under mild conditions. After moderate rearrangement and organization, image blocks can be represented as low-rank or approximately low-rank matrix. The intra prediction can then be formulated as a matrix completion problem, thus the unknown pixels can be inferred from limited samples with very high accuracy. Specifically, we novelly rearrange the encoded blocks similar to the current block to generate an observation matrix, from which the prediction can be obtained by solving a low-rank minimization problem. Experimental results demonstrate that the proposed scheme can achieve averagely 5.39% bit-rate saving for CIF sequences and 4.21% for QCIF sequences compared with standard H.264/AVC.

A practical algorithm for tanner graph based image interpolation

This paper interprets image interpolation as a decoding problem on tanner graph and proposes a practical belief propagation algorithm based on a gaussian autoregressive image model. This algorithm regards belief propagation as a way to generate and fuse predictions from various check nodes. A low complexity implementation of this algorithm measures and distributes the departure of current interpolation result from the image model. Convergence speed of the proposed algorithm is discussed. Experimental results show that good interpolation results can be obtained by a very small number of iterations.

Realistic mesh compression based on geometry image

In order to show the realistic 3D mesh in geometry image-based 3D mesh compression, in addition to coding geometry image, normal-map image is usually required to code. But normal-map image are difficult to compress because it captures more details of the original mesh, and it has less spatial correlation between pixels than geometry image. This paper proposes a novel coding framework to solve this problem, we effectively predict the normal-map image based on the correlation between geometry image and normal-map image, and we also utilize the strong correlation among three components of normal-map image to improve the predicting accuracy. In this framework we only need to code geometry image and residual image which generated from normal-map image and its prediction. Experimental results show that comparing with the method which coding geometry image and normal-map image using JPEG2000 directly, our coding framework not only improves the coding efficiency of geometry images and normal-map images, but also enhances the realistic effect of 3D mesh significantly.

A new image deblurring algorithm with less ringing artifacts via error variance estimation and soft decision

Image deblurring is an ill-posed linear inverse problem. Most traditional algorithms suffer from severe ringing artifacts. Recent approaches handle this issue by regularization techniques based on assumed image prior models. This paper presents a new method to reduce the ringing artifacts, without introducing any image prior models. For this purpose, we revisit the deblurring problem, using a probabilistic graph to model the image formation process. We establish the link between iterative back-projection and belief propagation and show that the ringing artifacts are caused by error propagation. Based on these analysis, we introduce a method to measure the variance of an estimation image and further propose an error-variance aware deblurring algorithm. Experimental results demonstrate that the proposed algorithm is very effective in suppressing the ringing artifacts.

A Fast Intra-mode Decision Scheme for HEVC

Compared with H.264/AVC, the newly published High Efficiency Video Coding (HEVC) standard improves the intra prediction by introducing much more prediction modes and novel techniques, which brings great computational burden. Although the rough mode decision (RMD) scheme in HEVC provides a way to speed up the process, it does not consider the similarity of neighboring modes to reduce the number of candidate modes. In this paper, we introduce a combined strategy for HEVC in intra prediction by utilizing parent PU candidate list, early termination of candidate list and mode group generation. Experimental results show that the proposed fast mode decision scheme significantly reduce the computation complexity with slight BD-rate increase, compared with the standard HM-9.0.