A Novel Macroblock Level Rate Control Method for Stereo Video Coding

To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods

Research Article Visual Perception Based Objective Stereo Image Quality Assessment for 3D Video Communication

Abstract: Stereo image quality assessment is a crucial and challenging issue in 3D video communication. One of major difficulties is how to weigh binocular masking effect. In order to establish the assessment mode more in line with the human visual system, Watson model is adopted, which defines visibility threshold under no distortion composed of contrast sensitivity, masking effect and error in this study. As a result, we propose an Objective Stereo Image Quality Assessment method (OSIQA), organically combining a new Left-Right view Image Quality Assessment (LR-IQA) metric and Depth Perception Image Quality Assessment (DP-IQA) metric. The new LR-IQA metric is first given to calculate the changes of perception coefficients in each sub-band utilizing Watson model and human visual system after wavelet decomposition of left and right images in stereo image pair, respectively. Then, a concept of absolute difference map is defined to describe abstract differential value between the left and right view images and the DP-IQA metric is presented to measure structure distortion of the original and distorted abstract difference maps through luminance function, error sensitivity and contrast function. Finally, an OSIQA metric is generated by using multiplicative fitting of the LR-IQA and DP-IQA metrics based on weighting. Experimental results shows that the proposed method are highly correlated with human visual judgments (Mean Opinion Score) and the correlation coefficient and monotony are more than 0.92 under five types of distortions such as Gaussian blur, Gaussian noise, JP2K compression, JPEG compression and H.264 compression

ESPRIT-like two-dimensional direction finding for mixed circular and strictly noncircular sources based on joint diagonalization

In this paper, a two-dimensional (2-D) direction-of-arrival (DOA) estimation method for a mixture of circular and strictly noncircular signals is presented based on a uniform rectangular array (URA). We first formulate a new 2-D array model for such a mixture of signals, and then utilize the observed data coupled with its conjugate counterparts to construct a new data vector and its associated covariance matrix for DOA estimation. By exploiting the second-order non-circularity of incoming signals, a computationally effective ESPRIT-like method is adopted to estimate the 2-D DOAs of mixed sources which are automatically paired by joint diagonalization of two direction matrices. One particular advantage of the proposed method is that it can solve the angle ambiguity problem when multiple incoming signals have the same angle θ or β. Furthermore, the theoretical error performance of the proposed method is analyzed and a closed-form expression for the deterministic Cramer-Rao bound (CRB) for the considered signal scenario is derived. Simulation results are provided to verify the effectiveness of the proposed method

Fast Macroblock Mode Selection Algorithm for Multiview Video Coding

Multiview video coding (MVC) plays an important role in three-dimensional video applications. Joint Video Team developed a joint multiview video model (JMVM) in which full-search algorithm is employed in macroblock mode selection to provide the best rate distortion performance for MVC. However, it results in a considerable increase in encoding complexity. We propose a hybrid fast macroblock mode selection algorithm after analyzing the full-search algorithm of JMVM. For nonanchor frames of the base view, the proposed algorithm halfway stops the macroblock mode search process by designing three dynamic thresholds. When nonanchor frames of the other views are being encoded, the macroblock modes can be predicted from the frames of the neighboring views due to the strong correlations of the macroblock modes. Experimental results show that the proposed hybrid fast macroblock mode selection algorithm promotes the encoding speed by 2.37 ∼ 9.97 times without noticeable quality degradation compared with the JMVM

Hierarchical complexity control algorithm for HEVC based on coding unit depth decision

Abstract The next-generation High Efficiency Video Coding (HEVC) standard reduces the bit rate by 44% on average compared to the previous-generation H.264 standard, resulting in higher encoding complexity. To achieve normal video coding in power-constrained devices and minimize the rate distortion degradation, this paper proposes a hierarchical complexity control algorithm for HEVC on the basis of the coding unit depth decision. First, according to the target complexity and the constantly updated reference time, the coding complexity of the group of pictures layer and the frame layer is allocated and controlled. Second, the maximal depth is adaptively assigned to the coding tree unit (CTU) on the basis of the correlation between the residual information and the optimal depth by establishing the complexity-depth model. Then, the coding unit smoothness decision and adaptive low bit threshold decision are proposed to constrain the unnecessary traversal process within the maximal depth assigned by the CTU. Finally, adaptive upper bit threshold decision is used to continue the necessary traversal process at a larger depth than the maximal depth of allocation to guarantee the quality of important coding units. Experimental results show that our algorithm can reduce the encoding time by up to 50%, with notable control precision and limited performance degradation. Compared to state-of-the-art algorithms, the proposed algorithm can achieve higher control accuracy

Intra Complexity Control Algorithm for VVC

Versatile Video Coding (VVC) has advantages over High Efficiency Video Coding (HEVC); it can save nearly half of the bit rate and significantly improve the compression efficiency, but VVC’s coding complexity is extremely high. Therefore, VVC encoders are difficult to implement in video devices with different computing capabilities and power constraints. In this paper, we apply texture information and propose a VVC intra complexity control algorithm. The algorithm assigns a different encoding time to each CU based on the corresponding texture entropy. Besides, the complexity reduction strategy at the CU level is designed to balance the complexity control while taking rate-distortion performance into consideration. Experiments in our paper show that the coding complexity can be accurately controlled from 90% to 70% with a slight loss of RD performance

Video quality assessment using motion-compensated temporal filtering and manifold feature similarity.

Well-performed Video quality assessment (VQA) method should be consistent with human visual systems for better prediction accuracy. In this paper, we propose a VQA method using motion-compensated temporal filtering (MCTF) and manifold feature similarity. To be more specific, a group of frames (GoF) is first decomposed into a temporal high-pass component (HPC) and a temporal low-pass component (LPC) by MCTF. Following this, manifold feature learning (MFL) and phase congruency (PC) are used to predict the quality of temporal LPC and temporal HPC respectively. The quality measures of the LPC and the HPC are then combined as GoF quality. A temporal pooling strategy is subsequently used to integrate GoF qualities into an overall video quality. The proposed VQA method appropriately processes temporal information in video by MCTF and temporal pooling strategy, and simulate human visual perception by MFL. Experiments on publicly available video quality database showed that in comparison with several state-of-the-art VQA methods, the proposed VQA method achieves better consistency with subjective video quality and can predict video quality more accurately

Fen CHEN,

Abstract: This paper proposes a new adaptive image quality assessment (AIQA) method, which is based on distortion classifying. AIQA contains two parts, distortion classification and image quality assessment. Firstly, we analysis characteristics of the original and distorted images, including the distribution of wavelet coefficient, the ratio of edge energy and inner energy of the differential image block, we divide distorted images into White Noise distortion, JPEG compression distortion and fuzzy distortion. To evaluate the quality of first two type distortion images, we use pixel based structure similarity metric and DCT based structural similarity metric respectively. For those blurriness pictures, we present a new wavelet-based structure similarity algorithm. According to the experimental results, AIQA takes the advantages of different structural similarity metrics, and it’s able to simulate the human visual perception effectively. Copyright © 2014 IFSA Publishing, S. L