Xing Wen

Perceptual compressive sensing for image signals

Human eyes have different sensitivity to different frequency components of image signals, typically, low frequency components are relatively more crucial to the perceptual quality of images than high frequency components. Based on this observation, we propose a novel sampling scheme for compressive sensing framework by designing a weighting scheme for the sampling matrix. By adjusting the weighting coefficients, we can tune the structure of the sampling matrix to favor the frequency components that are important to human perception, so that those components could be more precisely recovered in the reconstruction procedure. Experimental results reveal that our proposed scheme can greatly enhance the performance of compressive sensing framework in both PSNR and visual quality without increasing the complexity of the framework structure or computational procedure.

Novel 2-D MMSE Subpixel-Based Image Down-Sampling

Subpixel-based down-sampling is a method that can potentially improve apparent resolution of a down-scaled image on LCD by controlling individual subpixels rather than pixels. However, the increased luminance resolution comes at price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. We propose a new subpixel-based down-sampling pattern called diagonal direct subpixel-based down-sampling (DDSD) for which we design a 2-D image reconstruction model. Then, we formulate subpixel-based down-sampling as a MMSE problem and derive the optimal solution called minimum mean square error for subpixel-based down-sampling (MMSE-SD). Unfortunately, straightforward implementation of MMSE-SD is computational intensive. We thus prove that the solution is equivalent to a 2-D linear filter followed by DDSD, which is much simpler. We further reduce computational complexity using a small k × k filter to approximate the much larger MMSE-SD filter. To compare the performances of pixel and subpixel-based down-sampling methods, we propose two novel objective measures: normalized l1 high frequency energy for apparent luminance sharpness and PSNRU(V) for chrominance distortion. Simulation results show that both MMSE-SD and MMSE-SD(k) can give sharper images compared with conventional down-sampling methods, with little color fringing artifacts.

Reweighted Compressive Sampling for image compression

Compressive Sampling (CS), is an emerging theory which points us a promising direction of designing novel efficient data compression techniques. However, the conventional CS adopts a non-discriminated sampling scheme which usually gives poor performance on realistic complex signals. In this paper we propose a reweighted Compressive Sampling for image compression. It introduces a weighting scheme into the conventional CS framework whose coefficients are determined in encoding side according to the statistics of image signals. Experimental results demonstrate that our proposed method notably outperforms the conventional Compressive Sampling framework in coding performance in the sense that the reconstruction quality is greatly enhanced with same number of measurements and computational complexity.

Novel RD-Optimized VBSME With Matching Highly Data Re-Usable Hardware Architecture

To achieve superior performance, rate-distortion optimized motion estimation (ME) for variable block size (RDO VBSME) is often used in state-of-the-art video coding systems such as the H.264 JM software. However, the complexity of RDO-VBSME is very high both for software and hardware implementations. In this paper, we propose a hardware-friendly ME algorithm called RDOMFS with a novel hardware-friendly rate-distortion (RD)-like cost function, and a hardware-friendly modified motion vector predictor. Simulation results suggest that the proposed RDOMFS can achieve essentially the same RD performance as RDO-VBSME in JM. We also propose a matching hardware architecture with a novel Smart Snake Scanning order which can achieve very high data re-use ratio and data throughout. It is also reconfigurable because it can achieve variable data re-use ratio and can process variable frame size. The design is implemented with TSMC 0.18 μm CMOS technology and costs 103 k gates. At a clock frequency of 63 MHz, the architecture achieves real-time 1920 × 1080 RDO-VBSME at 30 frames/s. At a maximum clock frequency of 250 MHz, it can process 4096 × 2160 at 30 frames/s.

A new adaptive subpixel-based downsampling scheme using edge detection

In this paper, a new adaptive subpixel-based downsampling scheme is proposed. Inside this scheme, we take full advantage of subpixels by adaptively choosing the sample directions based on edge information which has not been addressed before. Then, an adaptive filter is designed to suppress color fringing artifacts. Moreover, a good cut-off frequency is derived and deployed in our filter to obtain extra information. Simulation results illustrate that the proposed adaptive subpixel-based downsampling scheme successfully improves the resolution while efficiently removes visible color fringing artifacts.

Color video denoising based on adaptive color space conversion

Denoising is one of the most common and important task in video processing systems and abundant efforts have been made on video denoising nowadays. Multihypothesis motion compensated filter (MHMCF) is an effective video denoising method, which combines multiple hypotheses obtained from motion estimation through a number of reference frames by weighted average to suppress noise. However, MHMCF only considers denoising of grayscale video signal. In this paper, we apply MHMCF to color video denoising, where the RGB video is first transformed to the luminance-color difference space before denoising. Instead of using traditional YCbCr color conversion, we propose a novel color conversion matrix which is adaptive to the noise variance in R, G, B channels. Simulation results demonstrate that our proposed color space conversion method can successfully improve the denoising performance for color video.

Joint Demosaicing and Subpixel-Based Down-Sampling for Bayer Images: A Fast Frequency-Domain Analysis Approach

A portable device such as a digital camera with a single sensor and Bayer color filter array (CFA) requires demosaicing to reconstruct a full color image. To display a high resolution image on a low resolution LCD screen of the portable device, it must be down-sampled. The two steps, demosaicing and down-sampling, influence each other. On one hand, the color artifacts introduced in demosaicing may be magnified when followed by down-sampling; on the other hand, the detail removed in the down-sampling cannot be recovered in the demosaicing. Therefore, it is very important to consider simultaneous demosaicing and down-sampling.

An efficient motion vector coding algorithm based on adaptive predictor selection

Motion Estimation is a core part of modern video coding standards, which significantly improves the compression efficiency. On the other hand, motion information takes considerable portion of compressed bit stream, especially in low bit rate situation. In this paper, an efficient motion vector prediction algorithm is proposed to minimize the bits used for coding the motion information. Several spatial and temporal neighboring motion vectors are selected as the motion vector predictor (MVP) candidates. By applying template matching to each block, a near-optimal MVP can be obtained both at the encoder and decoder side, thus no predictor index is needed to signal to the decoder. We also embed the MVP into current motion estimation process. Furthermore, a correction technique is executed as a remedy when template matching picks out a non-efficient predictor. Simulation results indicate that a bit rate reduction of up to 7.29% over H.264/AVC is achieved by the proposed scheme.

Adaptive Bilateral Filter Considering Local Characteristics

In this paper, we propose a simple but effective adaptive bilateral filter considering local characteristics. The presented method exploits the local gaussian gradient information of the processing image and applies bilateral filter with changing the range filter parameter

Edge-based Adaptive Directional Intra Prediction

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