Guoqing Xiang

An adaptive inter CU depth decision algorithm for HEVC

The emerging High-Efficiency Video Coding (HEVC) standard has introduced a number of new coding tools, such as a quad-tree based coding unit (CU). The quadtree-structured coding unit achieves significant coding efficiency improvements compared to H264/AVC. However, the complexity of CU depth decision associated with Rate-Distortion (R-D) cost computation dramatically increased. In order to alleviate the computational burden in HEVC inter coding, a fast CU depth decision algorithm is proposed in this paper. Firstly, zero CU detection method for HEVC is proposed as early termination algorithm. Secondly, the CU depth pruning strategies are adaptively determined according to standard deviation of statistic spatiotemporal depth information. Finally, when the neighbors are not available or have a very weak correlation, edge gradient of current coding tree unit (CTU) is considered as main factor for CU depth pruning method. Experimental results demonstrate that, compared with the original HM16.0 implementation, the proposed algorithm achieves about 40.5% encoding time saving with ignorable coding performance degradation.

An improved adaptive quantization method based on perceptual CU early splitting for HEVC

High Efficiency Video Coding is the latest video coding standard, and it can achieve significant compression performance with numerous coding tools. It supports different quantization parameters for each different coding unit in any depth coding unit. However, the adaptive quantization (AQ) method in current HEVC suffers from the underestimated spatial activity and Lagrange multiplier selection problems. Whats more, no perceptual considering are taken into the AQ model, thus it cannot obtain performance correctly and reasonably. Therefore, in this paper, we first solve the problems existed in current AQ method and then we propose a novel perceptual AQ method (PAQ) based on an adaptive perceptual CU early splitting algorithm to improve performance. Experiments demonstrate that with SSIM (Structure Similarity) as metric, it can achieve more than −9.3% BD-Rate saving with proposed method compared with the HM10.0 encoder, which outperforms than the existed methods.

An Adaptive Perceptual Quantization Algorithm Based on Block-Level JND for Video Coding

It has been widely demonstrated that integrating efficient perceptual measures into traditional video coding framework can improve subjective coding performance significantly. In this paper, we propose a novel block-level JND just-noticeable-distortion model, which has not only adjusted pixel-level JND thresholds with more block characteristics, but also integrated them into a block-level model. And the model has been applied for adaptive perceptual quantization for video coding. Experimental results show that our model can save bit rates up to 24.5% on average with negligible degradation of the perceptual quality.

Adaptive feature selection based on local descriptor distinctive degree for vehicle retrieval application

Performing image retrieval in large image database is becoming popular with the development of multimedia technology. Recently, ISO/IEC moving pictures experts group (MPEG) drafts compact descriptors for visual search (CDVS) to support the related applications. The state-of-the-art feature selection strategy in CDVS adopts a priori relevance measure to guide the selection. However, this method ignores the gradient information of the described feature. In this paper, we adopt CDVS to address traffic vehicle search in large database. After detailedly analyzing to the local descriptor, we firstly define local descriptor distinctive degree based on the gradient quantity and the spatial gradient distribution energy of the feature. Then we propose our adaptive feature selection method by combing feature distinctive degree and the priori information. The proposed method is proven to be better than the standard algorithm in CDVS and it almost introduces 10% mean average precision (MAP) and top match rate increase in low bit rate mode.

A Multi-exposure Fusion Method Based on Locality Properties

A new method is proposed for fusing a multi-exposure sequence of images into a high quality image based on the locality properties of the sequence. We divide the images into uniform blocks and use variance to represent the information of blocks. The richest information RI image is computed by piecing together blocks with largest variances. We assume that images in the sequence are high dimensional data points lying on the same neighbourhood and borrow the idea from locally linear embedding LLE to fuse a result image which is closest to the RI image. The result is comparable to the state-of-art tone mapping operators and other exposure fusion methods.

A novel adaptive quantization method for video coding

In this paper, we propose a novel AQ (Adaptive Quantization) algorithm to improve the subjective coding performance. Firstly, the factors affecting a suitable adaptive quantization method are carefully analyzed. Two important conclusions are drawn to guide designing a suitable AQ method. Secondly, based on the drawn analysis, a novel SATD (Sum of the Transformed Difference) based temporal adaptive quantization method is proposed. The proposed method fully considers the temporal characteristics, which can produce more visual-friendly QP (quantization parameter) offset distribution. Experiments are performed on x264 and HM16.0, respectively. With SSIM (Structure Similarity) as the metric, more than 21.07% BD-Rate (Bjontegaard-Delta Rate) saving can be achieved on x264, and on HM16.0, 8.07% and 7.99% BD-Rate savings can be obtained for LDP (Low-Delay-main-P) and LDB (Low-Delay-main-B) configurations, respectively, which is better than the state-of-the-art methods.

A perceptually temporal adaptive quantization algorithm for HEVC

Abstract Adaptive quantization (AQ) proves to be an effective coding tool to improve the performance of video coding. This paper presents a perceptually temporal AQ method to improve the subjective coding performance for High Efficiency Video Coding (HEVC). We first put forward a perceptual quality oriented motion estimation algorithm, which is conducted with a spatial-temporal just noticeable distortion (JND) model. Then one perceptual feature in temporal domain is proposed to develop our AQ method, which can generate different quantization parameter (QP) offsets for each coding unit (CU). The proposed method fully utilizes the temporal and perceptual characteristics of each CU, which can produce more visual-friendly QP offsets distribution. Experiments are conducted on HM16.0 (HEVC reference software), and with SSIM (Structure Similarity Index Metric) as the distortion metric, more than 8.08% and 7.95% rate savings can be obtained for Low-Delay-P (LDP) and Low-Delay-B (LDB) configurations on average, respectively. The subjective quality evaluation demonstrates that the proposed AQ method can achieve comparable visual quality as the HM16.0 while the proposed method can yield remarkable bitrate reductions.

Fast rate distortion optimized quantization method for HEVC

Rate-Distortion Optimized Quantization (RDOQ) brings significant improvement of coding performance in High Efficiency Video Coding (HEVC). However, it results in high computational complexity to determine the best quantization levels for each transform coefficient when applying rate and distortion optimization operation. In this paper, we firstly proposed an efficient way to skip the all zero coding units. Then we further propose a fast RDOQ scheme by skipping the optimization step of All Quantized Zero Blocks except DC Coefficient (AQZB-DC). Moreover, a rate difference model is established to select optimal quantization level for AQZB-DC blocks. Experiments are carried out on reference software HM 16.0 and the results show that the proposed method achieves 42.00% and 39.63% quantization time saving under Random Access (RA) and Low Delay (LD) configuration on average, while the BD-Rate loss is only 0.03% and 0.06%, respectively.

Adaptive perceptual preprocessing for video coding

The quantization of block DCT coefficients is too coarse, and will result in much non-uniform artifacts, therefore, blocking and ring artifacts are usually visible in reconstructed video frames especially when the bitrate is not sufficient. In this paper, we present an adaptive perceptual preprocessing (APP) method to reduce the probability of these artifacts generated in video encoding. The APP algorithm employs a novel filter based on just noticeable distortion filter (JNDF) and adaptive bilateral filter (ABF), and they can be adaptively chosen by block characteristics and quantization parameters. Experimental results demonstrate our proposed algorithm can significantly improve the subjective quality of reconstructed image.

Hardware-oriented adaptive multi-resolution motion estimation algorithm and its VLSI architecture

In this paper, we propose a hardware architecture of an adaptive multi-resolution motion estimation algorithm (AMMEA) for high definition video encoder to reduce hardware cost. The texture-based search strategies are based on temporal stationarity and spatial homogeneity with Sobel edge operator. The proposed algorithm makes motion estimation more concise. We also propose Sobel edge operator hardware architecture. The four-pixel SAD unit which is the basic processing element (PE) in our proposed architecture is used for SAD calculation and Sobel edge operator computation. The hardware architecture achieves very high data utilization and data throughout. Using our proposed AMMEA with regular data flow, simulation results show that the proposed architecture can significantly reduce the hardware cost with a negligible PSNR loss of 0.03dB compared with the full-search. The design is implemented with SMIC 0.18μm CMOS technology and costs 950K gates count, and it supports the real-time encoding of 1080P@30fps with two reference frames under a clock frequency of 150MHz.