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The generated bit rate with the proposed rate control technique.
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Rate control plays a very important role in constant bit rate (CBR) coding. AVC standard is jointly developed by ISO and ITU-T, which contains several inter and intra prediction modes. Rate distortion optimization (RDO) based on prerequisite quantization parameters determines the optimal prediction of each macroblock. This makes the current AVC sof...
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... developed by JVT serves as the platform [9]. Table 1 illustrates the coding results of the proposed rate control scheme. The sequence format and testing conditions are also shown in Table 1. ...
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Rate control algorithms (RCAs) aim to achieve the best visual quality under the minimum bit rate and the limited buffer size. A self-parameter-tuning fuzzy-PID controller is proposed to reduce the deviation between the target buffer level and the current buffer fullness. Fuzzy logic is used to tune each parameter of the proportional-integral-deriva...
Citations
... Pour l'estimation de mouvement, le critère de débit-distorsion est exprimé de la manière suivante : Suite au calcul du vecteur de mouvement, le mode de prédiction des résidus est choisi en utilisant le même mécanisme. Le critère de débit-distorsion est exprimé de la manière suivante : Le choix des valeurs de λ mvt et λ mode est un enjeu majeur de l'optimisation Lagrangienne, et plusieurs approches ont été proposées [93,50]. L'approche adoptée dans le codeur de référence de MPEG-4 AVC/H.264 ...
... L'approche adoptée dans le codeur de référence de MPEG-4 AVC/H.264 est celle de [50], qui définit les deux paramètres comme suit : ...
... Certaines approches raffinent le contrôle au niveau des macroblocs [50,47]. Elles sont axées sur les performances du contrôle en termes de précision et d'adaptation aux changements d'activité dans les données à coder. ...
This work is about designing bit rate control technics for the MPEG-4 Scalable Video Coding standard. The proposed approach benefits from a low computational complexity, so that its impact on the encoding time is very small. The quality of the encoded stream is also considered when controlling the bitrate in order to obtain optimal visual impression. With today's heterogeneous communication technologies and video-reading devices, broadcasting video content requires large amounts of time and money to provide the user with optimal quality in any context. Scalable video coding has been developed as an answer to this need for adaptive video streams. In 2008, the MPEG-4 Scalable Video Coding (SVC) extension was finalized. To address the adaptivity issues, this new standard provides three types of scalabilities. Spatial, temporal and quality scalabilities allow a video stream to adapt the dimensions, the number of frames per second and the fidelity depending on the target's requirements. Bit rate control is an important part of the encoding process as it allows to adapt the bitrate at the output of the encoder to the target's constraints. Unlike most of the existing approaches, our rate control technic is designed to have a low computational complexity. First, a bit rate per second constraint is specified for each video layer. Then, the available bits are dispatched among frames so that constant quality can be achieved in the decoded stream. The respect of the specified budget is finally enforced by a bit rate model, based on a simple and effective framework called $\rho$-domain. Using the statistics of the frame, this model allows us to choose the optimal quantization parameter while maintaining the amount of calculations very low. Based on this simple technic, we propose two approaches. In the first approach, each image is pre-encoded to provide the bit rate model with the statistics of the frame before rate control. This approach allows us to regulate the bit rate with great accuracy, as the bit rate error is below 3\% of the specified constraint on each type of scalability. In the second approach, rate control is performed using the information from the previous frame, so that no pre-encoding step is required and each frame is encoded only once. Thus, the impact on the encoding process is greatly reduced, at the cost of a small error increase. Finally, perceptual quality is considered when dispatching the bits among frames, so that quality variations are reduced and visual experience is improved. Our bit rate control technic for MPEG-4 SVC shows great accuracy on all spatial, temporal and quality scalabilities. Its low complexity, together with quality variation control make it a valuable contribution, especially in practical applications, for which time ressources are limited and the user-felt quality is important.
... The H.263 Test Model Version 8 (TMN8) uses a RCA that allows control at the frame level and MB level [2]. This algorithm operates based on a second-order R-D model which is parameterized based on variance of the luminance and chrominance values in the (motion-compensated or intra) MB [3]. The MPEG-4 Verification Models (VM5) describes a RCA [4], which offers rate control at the frame level. ...
A novel semi-fuzzy (SF) rate control algorithm (RCA) for variable bit rate (VBR) video applications is proposed. The proposed RCA is optimized to provide high quality compressed video bit streams in a wide operating range from constant quality to nearly constant bit rate. Thanks to a low degree of computational complexity, it is suitable for real-time applications of VBR video. The proposed RCA operates under given buffer size, delay and quality constraints. It provides a VBR video bit stream by controlling the quantization parameter (QP) on a picture basis. The QP is mainly controlled by a fuzzy rate controller and a deterministic quality controller, which are optimized such that they minimize the variation of quality to provide encoded video with high and stable visual quality. The proposed RCA has been implemented in an H.264/AVC video codec and the experimental results show that it provides a high-level average quality for encoded video while strictly obeying the buffering delay and quality constraints.
... In comparison with other video standards, there are several challenges for rate control in H.264 [9][10][11][12], due to its unique features. The first one is the well-known chickenand-egg dilemma in the rate-distortion optimization (RDO) process [10], which is briefly described as follows. ...
... Although several rate control algorithms have recently been proposed to cope with these problems [9,12,14], the proper method for rate control in H.264/AVC has not been fully explored. A predictive rate control scheme [9] has been adopted in H.264/AVC reference software JM 9.3 [15]. ...
... Although several rate control algorithms have recently been proposed to cope with these problems [9,12,14], the proper method for rate control in H.264/AVC has not been fully explored. A predictive rate control scheme [9] has been adopted in H.264/AVC reference software JM 9.3 [15]. The general idea of the rate control scheme is as follows: after preencoding of the macroblock using the QP of previously encoded macroblock, the block activity is measured by the sum of absolute differences (SAD). ...
We present an efficient rate control strategy for H.264 in order to maximize the video quality by appropriately determining the quantization parameter (QP) for each macroblock. To break the chicken-and-egg dilemma resulting from QP-dependent rate-distortion optimization (RDO) in H.264, a preanalysis phase is conducted to gain the necessary source information, and then the coarse QP is decided for rate-distortion (RD) estimation. After motion estimation, we further refine the QP of each mode using the obtained actual standard deviation of motion-compensated residues. In the encoding process, RDO only performs once for each macroblock, thus one-pass, while QP determination is conducted twice. Therefore, the increase of computational complexity is small compared to that of the JM 9.3 software. Experimental results indicate that our rate control scheme with two-step QP determination but single-pass encoding not only effectively improves the average PSNR but also controls the target bit rates well.
... In H.264, for example, the full coding modes selection allocates all processing and computational resources. Therefore, most conventional bit allocation methods [15,16], employing the H.264 video coding standard, do not use the full set of coding modes for providing real-time conditions or performing fast off-line storage encoding, which leads to lower video quality. ...
In this work, we present a novel approach for optimizing H.264/AVC video compression by dynamically allocating computational complexity (such as a number of CPU clocks) and bits for encoding each coding element (basic unit) within a video sequence, according to its predicted MAD (mean absolute difference). Our approach is based on a computational complexity–rate–distortion (C–R–D) analysis, which adds a complexity dimension to the conventional rate–distortion (R–D) analysis. Both theoretically and experimentally, we prove that by implementing the proposed approach for the dynamic allocation better results are achieved. We also prove that the optimal computational complexity allocation along with optimal bit allocation is better than the constant computational complexity allocation along with optimal bit allocation. In addition, we present a method and system for implementing the proposed approach, and for controlling computational complexity and bit allocation in real-time and off-line video coding. We divide each frame into one or more basic units, wherein each basic unit consists of at least one macroblock (MB), whose contents are related to a number of coding modes. We determine how much computational complexity and bits should be allocated for encoding each basic unit, and then allocate a corresponding group of coding modes and a quantization step-size, according to the estimated distortion (calculated by a linear regression model) of each basic unit and according to the remaining computational complexity and bits for encoding remaining basic units. For allocating the corresponding group of coding modes and the quantization step-size, we develop computational complexity–complexity step–rate (C–I–R) and rate–quantization step-size–computational complexity (R–Q–C) models.
... It performs very well and it is a low-complexity algorithm from the implementation points of view but it has many tuning parameters that should be adjusted for different applications. There are more other rate control algorithms such as [8], [9] which may be tuned to provide a kind of variable bitrate video but they are not suitable for our application for two reasons. First, they are too complex from the implementation point of view. ...
In this paper we propose a low-complexity fuzzy video rate controller designed for real-time variable bitrate applications with buffer constraints. The algorithm is optimized for streaming application in mobile devices. Furthermore, the proposed algorithm can be used for local recording application so as the recorded video may be streamed in future. Today, many mobile phones include a digital camera that can be used to capture and encode video in real-time. We assume that no memory for storage of uncompressed video is available in mobile phones. Therefore, look-ahead and multi-pass rate controls are not possible. Furthermore, considering the processing power and, more importantly, battery life constraints in mobile devices, the proposed algorithm needs to be as simple as possible. The described variable bitrate (VBR) bit rate control algorithm controls the quantization scale (QS) on picture basis. The QS is mainly controlled by a fuzzy controller such that it minimizes the variation of QS to provide encoded video with high visual quality so as to utilize the variable bitrate benefits as much as possible. The proposed rate control algorithm (RCA) has been implemented in the MPEG-4, H.263 and H.264/AVC standard video codecs and the experimental results show that it provides high level average quality for encoded video while it strictly obeys buffering constraints.
... Then, in the second loop, the actual encoding and reconstruction are performed. In [15], a modified TM5 rate control was used for H.264/AVC. This method first uses the QP of the previous MB in mode decision and in calculating the activity of the current MB. ...
... Finally, the mode decision is performed again with the refined QP. From the complexity point of view, both [14] and [15] are two-pass or at least, partially two-pass, algorithms, which may not be well suited for real time low delay applications. The work in [6], [19], however, is a one-pass rate control scheme. ...
... Next, further changes are made to to avoid overflow and underflow. Specifically, the final target is bounded as (14) where and are computed by otherwise (15) otherwise (16) is a constant with a typical value of 0.9. Equations (14)-(16) are similar to those of [6], [19]. ...
This paper presents an efficient rate control scheme for the H.264/AVC video coding in low-delay environments. In our scheme, we propose an enhancement to the buffer-status based H.264/AVC bit allocation method. The enhancement is by using a PSNR-based frame complexity estimation to improve the existing mean absolute difference based (MAD-based) complexity measure. Bit allocation to each frame is not just computed by encoder buffer status but also adjusted by a combined frame complexity measure. To prevent the buffer from undesirable overflow or underflow under small buffer size constraint in low delay environment,the computed quantization parameter (QP) for the current MB is adjusted based on actual encoding results at that point. We also propose to compare the bits produced by each mode with the average target bits per MB to dynamically modify Lagrange multiplier (λ<sub>MODE</sub>) for mode decision. The objective of QP and λ<sub>MODE</sub> adjustment is to produce bits as close to the frame target as possible, which is especially important for low delay applications. Simulation results show that the H.264 coder, using our proposed scheme, obtains significant improvement for the mismatch ratio of target bits and actual bits in all testing cases, achieves a visual quality improvement of about 0.6 dB on the average, performs better for buffer overflow and underflow,and achieves a similar or smaller PSNR deviation.
... Congestion control for live video coding over wireless networks can be traced back to rate control, where the encoder adjusts the quantization step size on a per-frame or permacroblock basis to meet a target average rate. Many rate control algorithms have been proposed for different video coding standards, such as TM5 for MPEG-2 [2] and [3] for H.264/AVC. We, instead, seek to optimize the tradeoff between network congestion and video distortion as in [4], for multi-user video streaming over wireless networks. ...
When multiple video sources are live-encoded and transmit- ted over a common wireless network, each stream needs to adapt its encoding parameters to wireless channel fluctua- tions, so as to avoid congesting the network. We present a stochastic system model for analyzing multi-user con- gestion control for live video coding and streaming over a wireless network. Variations in video content complexities and wireless channel conditions are modeled as indepen- dent Markov processes, which jointly determine the bottle- neck queue size of each stream. Interaction among multiple users are captured by a simple model of random traffic con- tention. Using the model, we investigate two distributed congestion control policies: an approach based on stochas- tic dynamic programming (SDP) and a greedy heuristic. Compared to fixed-quality coding with no congestion con- trol, performance gains in the range of 0.5-1.3 dB in average video quality are reported for the optimized schemes from simulation results.
... In the competing scheme, a transport-layer agent at each sender calculates the target rate from estimated round trip time and packet loss ratio based on the TCP-friendly Rate Control equation [1]. At the application layer, the rate control algorithm in the H.264 reference encoder [12] is invoked to meet this target. In both schemes, quantization adaptation is only performed for active frames, whereas inactive frames are always coded at a basic quality around 27 dB using fixed quantization. ...
One of the challenges in supporting multiple video streams over a small wireless network is appropriate rate control at each video source. Two rate adaptation approaches are proposed in this paper, to meet both quality and latency requirements while achieving overall efficiency. Encoding frame rate is adapted according to the importance of the video contents; quantization parameter for each frame is dynamically adjusted based on complexity of the current frame and past observations of end-to-end delay. Simulation results for a multi-camera surveillance application show the effectiveness of the proposed network-adaptive rate control in a live coding and streaming scenario. Compared to conventional methods combining TCP-friend rate control (TFRC) at the transport layer and video encoder rate control at the application layer, the proposed scheme improves the average PSNR of received video by 0.3-3.5 dB while also achieving less fluctuation in the video quality
... The intra_16x16 which supports 4 prediction modes performs prediction of the whole macroblock and is suited for smooth area, while the intra_8x8 and intra_4x4 which performs 8×8 and 4×4 block respectively support 9 prediction modes and are suited for detailed part of the picture. The best prediction mode(s) are chosen utilizing the R-D optimization[5] which is described as: ...
Rate-distortion optimization is the key technique in video coding standards to efficiently determine a set of coding parameters. In the R-D optimization for H.264 I-frame encoder, the distortion (D) is measured as the sum of the squared differences (SSD) between the reconstructed and the original blocks, which is same as MSE. Recently, a new image measurement called Structural Similarity (SSIM) based on the degradation of structural information was brought forward. It is proved that the SSIM can provide a better approximation to the perceived image distortion than the currently used PSNR (or MSE). In this paper, a new rate-distortion optimization for H.264 I-frame encoder using SSIM as the distortion metric is proposed. Experiment results show that the proposed algorithm can reduced 2.2~6.45% bit rate while maintaining the perceptual quality.
... For each picture with index in a GOP, parameters of the picture are estimated using previously encoded pictures as if is an I-type picture if is a P-type picture if is a B-type picture if is an I-type picture if is a P-type picture if is a B-type picture Now, we will explain the initialization and update of these estimators. As suggested in [41], we consider initialization of the frame bit allocation algorithm by choosing a frame quantization parameter for each frame type as follows: Define as the average number of bits targeted per a video frame pixel. It is calculated as where is the target bit rate, is the frame rate, and is the number of pixels per frame (e.g., for a 4:2:0 format QCIF sequence, ...
Based on the observation that a Cauchy density is more accurate in estimating the distribution of the ac coefficients than the traditional Laplacian density, rate and distortion models with improved accuracy are developed. The entropy and distortion models for quantized discrete cosine transform coefficients are justified in a frame bit-allocation application for H.264. Extensive analysis with carefully selected anchor video sequences demonstrates a 0.24-dB average peak signal-to-noise ratio (PSNR) improvement over the JM 8.4 rate control algorithm, and a 0.33-dB average PSNR improvement over the TM5-based bit-allocation algorithm that has recently been proposed for H.264 by Li et al. The analysis also demonstrates 20% and 60% reductions in PSNR variation among the encoded pictures when compared to the JM 8.4 rate control algorithm and the TM5-based bit-allocation algorithm, respectively.
