Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

360° images have attracted increasing attention due to their wide field of view. However, spherical 360° images need to be converted into 2D equi-rectangular projection (ERP) images for compression. This conversion often leads to pixel overstretching in the ERP image, which results in a lot of redundancy in the texture. Performing a direct prediction without considering the stretching will inevitably make it difficult to achieve optimal results. To tackle this problem, we propose a multi-layer adaptive scale-block scheme for ERP image compression. In particular, we introduce a multi-layer structure based on the overstretching rate and use multi-scale convolution kernels to better match each layer and extract features more effectively. Subsequently, we employ an adaptive scale-block method to effectively reduce bitrate redundancy in overstretched and less important regions. Finally, we propose a new end-to-end model that is efficient for 360° image compression. Experimental results demonstrate that our scheme outperforms other image compression methods and reduces bitrate by nearly 16% compared to the latest learned 360° image compression model.

360-degree videos (abbreviated as 360 videos) have gained widespread popularity due to their immersive experience. The massive amount of video data resulting from ultra-high resolution of 360 videos makes the coding process extremely complex and seriously limits their widespread applications. In this paper, we propose a new fast-Coding Unit (CU) partition algorithm for 360 videos based on Versatile Video Coding (VVC). The major novelty is that we establish databases and develop models based on stretching and split mode (SM) distribution for 360 videos on VVC. Specifically, first, we establish stretching-based CU partition databases for 360 videos. Second, we propose a new stretching-based multi-scale convolution kernel network structure and a corresponding synthetical loss function, which further considers both Rate-Distortion cost and imbalance issue. We then develop a unique multi-threshold selection scheme to select candidate SMs. Experimental results demonstrate that the proposed algorithm improves encoding speed by 69.63%, with only a 2.28% increase in Bjøntegaard Delta Bit Rate (BDBR), outperforming the state-of-the-art methods.

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.

Due to the diversity of terminal devices, Spatial Scalable High Efficiency Video Coding (SSHVC) is an efficient solution to meet this requirement. However, its coding process is very complex, which seriously prevents its wide applications. Therefore, it is very crucial to reduce coding complexity and improve coding speed. In this paper, we propose a Gaussian Distribution-based Mode Selection for Intra Prediction of SSHVC. We show that the rate distortion costs of Inter-layer Reference (ILR) mode and Intra mode are significantly different, and both follow a Gaussian distribution. Based on this discovery, we propose to use a Bayes decision rule to determine whether ILR is the best mode so as to skip Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses.

Scalable High Efficiency Video Coding (SHVC) is the extension of High Efficiency Video Coding (HEVC). In intra prediction for quality SHVC, a Coding Unit (CU) is recursively divided into a quadtree-based structure from the largest 64×64 CU to the smallest 8×8 CU, in which 35 intra prediction modes and Inter-Layer Reference (ILR) mode are checked to determine the best possible mode. This leads to very high coding efficiency but also results in an extremely high coding complexity. To improve coding speed while maintaining coding efficiency, in this paper, we propose a new efficient algorithm for fast intra prediction for enhancement layer in SHVC. First, temporal and spatial correlations, as well as their correlation degrees, are combined in a Naive Bayes classifier to predict depth probabilities and skip depths with low likelihood. Second, for a given depth candidate, we combine ILR mode probability with Partial Zero Blocks (PZBs) based on the Sum of Squared Differences (SSD) to determine whether the ILR mode is the best one. In that case, we can skip intra prediction, which requires very high complexity. Third, initial Intra Modes (IMs) are obtained through Sobel operator, and are combined with the relationship between IMs and their corresponding Hadamard Cost (HC) values to predict candidate IMs in Rough Mode Decision (RMD). Then, an analytical criterion of early termination is developed based on the HC values of two neighboring IMs in the Rate-Distortion Optimization (RDO) process. Finally, we combine depth probabilities and the distribution of residual coefficients at the current depth to early terminate depth selection. The proposed scheme can significantly decrease the complexity of depth determination while reducing the complexity of mode decision for a depth candidate. Our experimental results demonstrate that the proposed scheme can achieve a speed up gain of more than 80% in average, while maintaining coding efficiency.

The scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC). It introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2N×2N, inter2N×N, interN×2N, interN×N, inter2N×nU, inter2N×nD, internL×2N and internRx×2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2N×2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%.

The Scalable High Efficiency Video Coding (SHVC) has very high coding efficiency, but its computational complexity is also very high. This definitely limits its wide applications, particularly for real-time video applications. Therefore, it is crucial to improve the coding speed. In this research, we have proposed a new inter mode prediction algorithm for quality SHVC, in order to improve the coding speed while maintaining coding efficiency. First, we divide mode prediction into square mode prediction and non-square mode prediction. Second, in the square mode prediction, Inter-Layer Reference (ILR) and merge modes are predicted based on depth correlation. Moreover, ILR mode, merge mode and inter 2N×2N are early terminated based on Rate Distortion (RD) cost. Third, if the early termination condition cannot be satisfied, nonsquare modes are further predicted based on the distribution of residual coef-ficients. Experimental results have demonstrated that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.

As an extension of High Efficiency Video Coding (HEVC), the Scalable High Efficiency Video Coding (SHVC) introduces multiple layers with inter-layer predictions, which greatly increases the complexity on top of the already complicated HEVC encoder. In Intra prediction for Quality SHVC, Coding Tree Unit (CTU) allows recursive splitting into four depth levels, which considers 35 Intra prediction modes and interlayer reference (ILR) mode to determine the best possible mode at each depth level. This achieves the highest coding efficiency but incurs a substantially high computational complexity. In this paper, we propose a novel Intra prediction scheme to effectively speed up the enhancement layer Intra-coding in Quality SHVC. The new features of the proposed framework include: First, spatial correlation and its correlation degree are combined to predict most probable depth level candidates. Second, for a given depth candidate, based on the probabilities of the ILR mode, we check the ILR mode by examining the residual distribution based on skewness and kurtosis to determine whether the residuals follow a Gaussian distribution. In that case, the Intra prediction comparisons, which require a high complexity, are skipped. Third, during Intra prediction selection from 35 Intra prediction modes, spatial and inter-layer correlations are combined with the local monotonicity of the Hadamard costs associated with the modes in a small neighborhood, to examine only a portion of Intra prediction modes. Finally, a hypothesis testing on the currently selected depth level is performed to examine whether the residuals present significant differences within their block to early terminate depth selection. The proposed multi-step multistrategy scheme aims to minimize the number of depth selections while greatly reducing the mode decision complexity for a depth candidate in a hierarchical fashion. Our experimental results demonstrate that the proposed scheme can achieve a speedup gain of more than 75% in average on the test video sequences, while maintaining almost the same coding efficiency. .

Rate Control (RC) is one of the crucial techniques for video compression and transmission because it controls compression bitrates in order to obtain optimum encoding quality within the available budget of transmission bitrates. In this paper, we conduct a research on developing a new unbuffered real-time rate control for video compression. With the objective to improve the overall rate control performance, we have proposed an effective low-delay rate control algorithm which has two unique features. First, unlike traditional algorithms which adopt buffers in rate control, our algorithm doesn't use a buffer in rate regulation which can reduce the encoding delay and improve real-time response. Second, a new PID bit controller is proposed to directly control encoding bitrates. In addition, we also developed a simple but effective method for real-time target bit allocation. Numerous experimental results have demonstrated that, when compared with the rate control algorithm adopted in MPEG-4 standard, our proposed algorithm achieves more accurate rate regulation and improves average coding quality.

The emerging H.264 Scalable Video Coding (H.264/SVC) requires the rate control algorithm to regulate the output bit rate of all the coarse-grain-scalability, temporal, spatial and combined enhancement layers. In this research, we propose an incremental rate control algorithm for H.264/SVC. First, a Rate-Complexity-Quantization (R-C-Q) model is extended in scalable video coding based on our previous work on H.264/AVC. Second, a complexity measure for Intra-frames is used to precisely determine QPs (Quantization Parameters) for Intra-frames. Finally, we adopt an incremental approach to compute QPs of inter-frames and a Proportional + Integral + Derivative (PID) buffer controller to provide robust buffer control for each layer. Our simulation results demonstrate that, our algorithm outperforms JVT-W043 rate control algorithm by providing more accurate output bit rate for each layer, maintaining stable buffer fullness, reducing frame skipping and quality fluctuation, finally, improving the overall coding quality.

Due to the high performance in the rate control of MPEG-4, the quadratic Rate-Distortion model has been widely proved. The rate control module in the reference software of H.264/AVC directly inherits the quadratic model. However, the introduction of Rate Distortion Optimization (RDO) in H.264/AVC makes rate control more complex than previous standards. Based on theoretical derivation and extensive experiments, this paper analyzes the model error of the quadratic model and the prediction error of MAD in H.264/AVC. Simulation results demonstrate that our proposed linear MAD model achieves higher prediction accuracy than the quadratic mode does.

This paper proposes an object-level rate control algorithm to jointly controlling the bit rates of multiple video objects. Utilizing noncooperative game theory, the proposed rate control algorithm mimics the behaviors of players representing video objects. Each player competes for available bits to optimize its visual quality. The algorithm finds an ¿optimal solution¿ in that it conforms to the mixed strategy Nash equilibrium, which is the probability distribution of the actions carried by the players that maximizes their expected payoffs (the number of bits). The game is played iteratively, and the expected payoff of each play is accumulated. The game terminates when all of the available bits for the specific time instant have been distributed to video object planes (VOPs). The advantage of the proposed scheme is that the bidding objects divide the bits among themselves automatically and fairly, according to their encoding complexity, and with an overall solution that is strategically optimal under the given circumstances. To minimize buffer fluctuation and avoid buffer overflow and underflow, a proportional-integral-derivative (PID) control based buffer policy is utilized.

Rate control in H.264/AVC aims to achieve the best tradeoff between encoding quality and bandwidth while satisfying the buffer restriction. Due to the improving efficiency of intra-only rate control, we propose an effective rate control scheme for intra-only encoding. The proposed scheme employs a novel rate-distortion (RD) model, a new complexity measure, a precise quantization parameter (QP) calculation method, and a simple but effective model adaptation mechanism for intra-frames. Experimental results demonstrate that, compared with JVT-W042, the proposed algorithm achieves higher precise bit estimation, provides more robust buffer control, and improves coding quality.

This paper presents a novel rate control algorithm for H.264/AVC video coding. Pertinent to the algorithm, the paper introduces a joint R-D model, a coding bit estimation approach, a picture complexity measurement, and a model update method. Specifically, the proposed picture complexity measurement employs a Harmonic Mean based approach to predict the coding complexity of a frame. These techniques collectively enhance the overall rate control performance. Experimental results demonstrate that the proposed algorithm outperforms JVT-W042 in providing robust buffer control, reducing frame skipping, and improving coding quality.

Rate control regulates the output bit rate of a video encoder in order to obtain optimum visual quality within the available network bandwidth and to maintain buffer fullness within a specified tolerance range. In this paper, we propose a novel rate control scheme for H.264/AVC video compression with a number of new features. We first introduce a calculation approach of frame complexity based on the linear prediction theory. Then, we propose a joint rate-distortion model which is an integration of a liner rate-complexity model and an exponential rate-quantization model. Finally, we develop an effective target bit estimation approach. Experimental results show that, compared with JVT-W042, our scheme achieves more accurate rate regulation, provides robust buffer control, efficiently reduces frame skipping, and improves visual quality.

Rate control plays a crucial role for video communication applications. It ensures that the generated compressed bit streams satisfy bandwidth and buffer constraints. Rate control algorithms recommended by H.264/AVC adopt rate-distortion (R-D) models for inter-frames to determine quantization parameters (QPs) but not for intra-frames. Instead, they directly compute QPs without any considerations of bitrates and coding complexities for intra-frames. In order to obtain more accurate target bit prediction for intra-frames, we first introduce the geometry gradient information as a new complexity measure to accurately represent the complexities for intra-frames. Then, we propose a novel R-D model which is an integration of a linear rate-complexity model and an exponential rate-quantization model. Finally, we develop an accurate and robust intra-only rate control algorithm for H.264/AVC. Experimental results demonstrate that, compared with JVT-W042, the proposed algorithm achieves higher precise bit estimation, provides more robust buffer control, and also improves coding quality.

In this work, we investigate a working memory approach for efficient temporal prediction in H.264 video coding. After video frames are encoded, objects are extracted, analyzed, and indexed in a dynamic database which acts as a working memory for the H.264 video encoder. During the encoding process, objects with similar spatial characteristics are retrieved from the working memory and used for motion prediction of objects in the current video frame. This approach extends the multiple-frame estimation and provides a more generic framework for spatiotemporal prediction of video data. Our experimental results on surveillance video data demonstrate that the proposed approach is able to save the coding bit rate by up to 35% with a small computational overhead.

In this study, the authors propose a new rate-complexity-quantisation model and an incremental rate control algorithm for H.264/AVC video coding. One unique property of this algorithm is that, the picture complexity estimation and rate-quantisation modelling are jointly designed with an incremental rate control for P-frames. In addition, the proposed algorithm also introduces a number of efficient rate control techniques, including accurate rate control for intra-frames, enhanced proportional–integral–derivative (PID) buffer controller, and adaptive quantisation parameter determination for B-frames. The proposed algorithm has low computational complexity while providing robust rate control. Our extensive experimental results demonstrate that the proposed algorithm outperforms the current rate control algorithm adopted in the H.264/AVC reference software JM13.2 by achieving more accurate rate control, reducing frame skipping, depressing quality fluctuation and improving the overall coding quality by up to 2.83 dB.

In this paper, we present a robost frame level rate control algorithm for H.264/AVC compression standard. Particularly, we propose a simple yet effective approach to deal with B frames, including a PSNR-QP model and an adaptive QP computation method for B frames. The objective is to reduce PSNR variations among frames and improve subjective visual quality. To better handle buffer fullness and reduce buffer overflow/underflow, we develop an enhanced proportional- integral-derivative (PID) buffer controller. Our experimental results demonstrate that the proposed algorithm outperforms the JVT-W042 solution by achieving accurate rate regulation, reducing frame skipping, depressing quality fluctuations, and finally, improving coding quality up to 1.43 dB.

In this paper, we propose a new incremental rate control scheme for H.264/AVC video coding, which elegantly resolves the "Chicken and Egg" dilemma by eliminating the need of coding complexity prediction for inter-frames. The proposed scheme introduces a number of new features, including a rate- complexity-quantization model, accurate quantization parameter (QP) estimation for intra-frames, incremental QP calculation for inter-frames, and a basic unit level QP adjustment. Experimental results demonstrate that the proposed scheme outperforms the JVT-G012 solution by providing more accurate rate control, reducing frame skipping, and decreasing quality fluctuations, and finally, improving coding quality by up to 1.85 dB.

In this paper, we propose a novel rate control scheme for H.264/AVC standard, including a new coding complexity measure for intra-frames, a new rate-complexity-quantization (R-C-Q) model, an accurate quantization parameter (QP) estimation for intra-frames, an incremental-control-based QP calculation for inter-frames, and a bit-allocation-balancing technique. Our experimental results demonstrate that, the proposed scheme outperforms the JVT-G012 solution by providing more accurate QP prediction, reducing frame skipping, depressing quality fluctuations, and finally, improving coding quality.

Video streaming imposes high rate requirement and stringent constraints on resource limited mesh networks. In this work, we develop a distributed asynchronous particle swarm optimization (DAPSO) algorithm for resource allocation and performance optimization scheme for video communication over large-scale mesh networks. Unlike many network resource allocation performance optimization algorithms in the literatures which are able to handle convex network utility functions, the proposed scheme is able to handle generic nonlinear network utility functions. We will use a specific rate allocation and quality optimization problem for an example to demonstrate the efficiency of the proposed scheme and compare its performance with other algorithms, such as distributed gradient search.

Object-based video coding, such as MPEG-4, supports the encoding and manipulating of individual video object. Rate control algorithm for multiple video object coding shall adapt to the perceptual importance of the video object. For this purpose, we present a joint rate control algorithm for coding multiple video objects. The proposed algorithm is aiming to obtain superior quality for the video objects with higher perceptual importance, while squeezing bits from the video objects with lower perceptual importance. The quality gaps between video objects is regulated to the target level through the proportional-derivative feedback design.

In this paper, based on the modified Hopfield neural network, we present two variational PDEs (Partial Differential Equations) as the regularization terms to the image restoration model. One is based on a harmonic model and the other is based on a total variation model. Then, we propose two novel variational image restoration algorithms based on the Modified Hopfield Neural Network (MHNN). Both algorithms are aiming to restore the degraded images and preserve the edges with improved visual quality. The experimental results demonstrate that our proposed algorithms perform better than other known neural network based restoration algorithms.

Object based coding can potentially achieve a higher degree of compression and better visual quality. This paper proposes a multiple object rate control algorithm for video transmissions over wireless systems. The algorithm adopts an efficient bit allocation strategy to intelligently allocates more bits to important objects, while adapts to time-varying wireless channels. Simulation results for both wireless downlink and uplink channels demonstrate that the proposed algorithm effectively improves perceptual quality on important objects, significantly reduces VOP (Video Object Plane in MPEG-4) skipping and thereby, maintains the motion continuity in wireless video transmissions.

In this paper, we propose a joint source-channel region-based rate control algorithm for real-time video transmissions over wireless systems. During the video transmission, the channel throughput available to the video encoder in the wireless systems is inherently variable, due to the retransmission of the error packets using the automatic repeat request (ARQ) error control. The variable data rate of the wireless system is characterized by the packet-level Gilbert two-state Markov Model, the parameters of which are extracted from the statistical properties of the channel information obtained from the wireless channel simulator. The proposed algorithm adopts a fast but effective block-based segmentation method to extract the regions of interest. Unlike traditional bit allocation methods used in the region/content-based rate control, the algorithm exploits the most effective criteria "coding qualities" as quantitative factors to directly control bit allocation among different regions so as to achieve better visual quality in the regions of interest. The computational complexity of the algorithm is low making it suitable for real-time applications. Compared with the MPEG-4 rate control algorithm, our algorithm can effectively enhance the perceptual quality for the regions of interest and significantly reduce the number of frame skipping; thereby, improve the smoothness of the video.

We propose a novel rate control algorithm for real-time video transmission over wireless systems, which encompass power control technique, region-based rate control strategy and a macroblock-based segmentation method. By allocating more power as well as more bits to the regions of interest of a video frame and less power & fewer bits to the rest regions, the algorithm improves the visual quality of the regions of interest while saving bits, and also adapts to time-varying wireless channels. When compared with the fixed power MPEG-4 rate control algorithm, the proposed algorithm successfully improves perceptual quality, reduces frame skipping and enhances motion continuity in wireless video transmission, while the total power is kept at the original level.

The embedded multireference frames selection with variable block-size motion compensation model drastically increases the computational complexity of the H.264/AVC video coding standard. This paper proposes an adaptive hierarchical motion estimation (ME) algorithm for H.264/AVC with the objective of minimizing the complexity while maximizing the visual quality. The proposed algorithm is based on a framework that exploits the "history" of the motion intensity from a video sequence in order to control ME. The complexity and memory requirement for this meta information is low. The algorithm determines the motion intensity of a video sequence at three levels and accordingly employs different ME techniques. The results certify that the history-based hierarchical information can be very effective in improving the efficiency of ME.

Object-based coding can potentially achieve a higher degree of compression and better visual quality. The objects in a scene are not always synchronous, that is, they have different temporal resolutions. In some applications, it is more efficient to transmit asynchronous objects with different temporal rates so as to achieve a better tradeoff between temporal and spatial resolutions. This requires to balance the qualities of individual objects while ensuring an overall visual quality with a given bit rate. This paper proposes a rate control algorithm for multiple video object encoding, which is suitable for both synchronous and asynchronous transmissions. The algorithm aims to maximize scene quality with an accurate bit rate, while efficiently handling buffer fullness. Using an efficient bit allocation strategy, the algorithm achieves accurate target bit rates, provides good coding quality, and decreases buffer overflow/underflow. The proposed algorithm also allows flexible priority adjustment among multiple objects to ensure overall better visual perception. Designed primarily for asynchronous objects, the algorithm treats the synchronous objects as a special case. Experimental results demonstrate that, when giving suitable asynchronous video object planes rates, the proposed algorithm achieves good temporal-spatial tradeoff while yielding accurate rate regulation and effective buffer control.

One of the fundamental challenges in deploying multimedia systems, such as telemedicine, education, space endeavors, marketing, crisis management, transportation, and military, is to deliver smooth and uninterruptible flow of audio-visual information, anytime and anywhere. A multimedia system may consist of various devices (PCs, laptops, PDAs, smart phones, etc.) interconnected via heterogeneous wireline and wireless networks. In such systems, multimedia content originally authored and compressed with a certain format may need bit rate adjustment and format conversion in order to allow access by receiving devices with diverse capabilities (display, memory, processing, decoder). Thus, a transcoding mechanism is required to make the content adaptive to the capabilities of diverse networks and client devices. A video transcoder can perform several additional functions. For example, if the bandwidth required for a particular video is fluctuating due to congestion or other causes, a transcoder can provide fine and dynamic adjustments in the bit rate of the video bitstream in the compressed domain without imposing additional functional requirements in the decoder. In addition, a video transcoder can change the coding parameters of the compressed video, adjust spatial and temporal resolution, and modify the video content and/or the coding standard used. This paper provides an overview of several video transcoding techniques and some of the related research issues. We introduce some of the basic concepts of video transcoding, and then review and contrast various approaches while highlighting critical research issues. We propose solutions to some of these research issues, and identify possible research directions.

This paper proposes a joint source-channel rate control algorithm for real-time video transmissions over WCDMA systems. The algorithm encompasses a region-based bit allocation strategy and a macro block-based segmentation technique. To enhance the video quality while saving bits, the algorithm effectively allocates more bits to important parts of a video frame, and also adapts to time-varying WCDMA channels. Simulation results demonstrate that the proposed algorithm effectively improves perceptual quality, reduces frame skipping and, thereby, maintains the motion continuity in wireless video transmissions.

The embedded motion compensation model of the new H.264/AVC video coding standard dramatically increases the computational complexity of motion estimation. We propose a fast motion estimation algorithm using a hierarchical motion intensity structure to lower the computational complexity of the motion estimation in H.264/AVC. The proposed algorithm is mainly based on a multi-level motion intensity structure. It determines the motion intensity at three levels and, accordingly, uses different motion estimation techniques to find a more accurate, and faster, motion vector (MV). Experimental results show that the proposed algorithm provides promising performance in terms of the computational speedup and video reconstruction quality.

A multistage motion estimation scheme is proposed. The scheme extracts video characteristics by first performing an online video analysis separately for foreground and background regions. Motion parameters are extracted and passed to the next stage. The next stage includes a mathematical model for the block distortion surface (BDS) that enables the algorithm to accordingly adjust its search technique. The search is performed on a precise search area adaptive to the statistical property of the motion vector prediction error. Due to its self-tuning property, not only does the proposed scheme adapt to scenes by yielding better visual quality but it also yields a lower computational complexity, compared with the other predictive motion estimation algorithms on standard benchmark sequences.

This paper proposes a rate control algorithm for single and multiple objects video coding. The algorithm exploits prediction and feedback control to achieve accurate bit rate while maximizing the picture quality and simultaneously effectively handling buffer fullness. The algorithm estimates the bit budget of a frame based on its global coding complexity, and dynamically distributes the target bits for each object within a frame according to the object's coding complexity. Exploiting a novel buffer controller based on the proportional-integral-derivative (PID) technique used in automatic control systems, the algorithm effectively reduces the deviation between the current buffer fullness and the target buffer fullness, and minimizes the buffer overflow or underflow. The algorithm dynamically adjusts several parameters to further improve the system performance. A scene-change handling method is used to deal with scene changes. The combination of prediction and feedback control improves the adaptability of the rate controller under complicated environments; it also decreases the effect of random disturbance and the deviation caused by the variance between the real system and its statistical model. Overall, the proposed algorithm successfully achieves accurate target bit rate, provides promising coding quality, decreases buffer overflow/underflow and lowers the impact of a scene change.

Video scenes containing multiple objects can potentially achieve higher degree of compression and better visual quality with individual coding for each object. Video objects are not always synchronous, implying each object may have a separate temporal resolution. This paper proposes a rate control algorithm for multiple video object encoding. Using a novel bit allocation strategy, the algorithm achieves accurate target bit rate, provides good visual quality, and decreases buffer overflow/underflow. Experimental results for both synchronous and asynchronous multiple video object encoding demonstrate that, when compared with the existing rate control scheme recommended by the MPEG-4 standard, the proposed algorithm provide better temporal-spatial tradeoff with more accurate rate regulation.