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This paper presents a revised rate control scheme based on an improved frame complexity measure. Rate control adopted by both MPEG-4 VM18 and H.264/AVC use a quadratic rate–distortion (R–D) model that determines quantization parameters (QPs). Classical quadratic R–D model is suitable for MPEG-4 but it performs poorly for H.264/AVC because one of the important parameters, mean absolute difference (MAD), is predicted through a linear model, whereas the MAD used in MPEG-4 VM18 is the actual MAD. Inaccurately predicted MAD results in wrong QP and consequently degrades rate–distortion optimization (RDO) performance in H.264. To overcome the limitation of the existing rate control schemes, we introduce an enhanced linear model for predicting MAD, utilizing some knowledge of current frame complexity. Moreover, we propose a more accurate frame complexity measure, namely, normalized MAD, to replace the current use of MAD parameter. Normalized MAD has a stronger correlation with optimally allocated bits than that of the predicted MAD. To minimize video quality variations, we also propose a novel long-term QP limiter (LTQPL). Finally, a dynamic bit allocation scheme among basic units is implemented. Extensive simulation results show that our method, with inexpensive computational complexity added, improves the average peak signal-to-noise ratio (PSNR) and reduces video quality variations considerably. [Copyright &y& Elsevier]

In this paper, we put forward an effective and efficient no reference image blurriness assessment metric on the basis of local binary pattern (LBP) features. In this proposal, we reveal that part of the LBP histogram bins present monotonously with the degree of blurriness. The proposed method contains the following steps. Firstly, the LBP maps of an input image are extracted with multiple radiuses. And then, the frequency of pattern histogram is analyzed before part of bins are chosen as the features. In addition, we also take the entropy of these bins as another feature. Finally, we learn the extracted features to predict the image blurriness score. Validation of the proposed method is conducted on the blurred images of LIVE-II, CSIQ, TID2008, TID2013, LIVE3D IQA Phase I and LIVE3D IQA Phase II. Experimental results demonstrate that compared with the state-of-the-art image quality assessment (IQA) methods, the proposed algorithm has notable advantage in correlation with subjective perception and computational complexity. [ABSTRACT FROM AUTHOR]

• A long-term quality model for rate control in concurrent MVT is proposed. • Lyapunov-based dynamic programming algorithm is developed to solve our problem. • Long-term video quality is improved without reducing the delay performance. Concurrent multipath transmission provides an effective solution for streaming high-quality mobile videos in heterogeneous wireless networks. Rate control is commonly adopted in multimedia communication systems to fully utilize the available network bandwidth. This paper proposes a novel rate control for concurrent multipath video transmission. The existing rate control algorithms mainly adapt bit rate in the short-term pattern, i.e., without considering the long-term video transmission quality. We propose a long-term rate control scheme that takes into account the status of both the transmission buffer and video frames. First, a mathematical model is developed to formulate the non-convex problem of long-term quality maximization. Second, we develop a dynamic programming solution for online encoding bit rate control based on buffer status. The performance evaluation is conducted in a real test bed over LTE and Wi-Fi networks. Experimental results demonstrate that the proposed long-term rate control scheme achieves appreciable improvements over the short-term rate control schemes in terms of video quality and delay performance. [ABSTRACT FROM AUTHOR]

• To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains. • Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction. • The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in both objective and subjective reconstruction quality. The existing video compressed sensing (CS) algorithms for inconsistent sampling ignore the joint correlations of video signals in space and time, and their reconstruction quality and speed need further improvement. To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains. Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction. Experimental results show that, compared with the existing algorithms, the proposed algorithm achieves a peak signal-to-noise ratio gain of 1–3 dB and a multi-scale structural similarity gain of 0.01–0.03 at a low time complexity, and the reconstructed frames not only have good edge contours but also retain textural details. [ABSTRACT FROM AUTHOR]