Rate-distortion optimized frame dropping and scheduling for multi-user conversational and streaming video

We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversa- tional and streaming videos. We consider a scenario where conversational and streaming videos share the forwarding resources at a network node. Two buffers are setup on the node to temporarily store the packets for these two types of video applications. For streaming video, a big buffer is used as the associated delay constraint of the application is moderate and a very small buffer is used for conversational video to ensure that the forwarding delay of every packet is limited. A scheduler is located behind these two buffers that dynamically assigns transmission slots on the outgoing link to the two buffers. Rate-distortion side information is used to perform RD-optimized frame dropping in case of node overload. Sharing the data rate on the outgoing link between the con- versational and the streaming videos is done either based on the fullness of the two associated buffers or on the mean incoming rates of the respective videos. Simulation results showed that our proposed RD-optimized frame dropping and scheduling ap- proach provides significant improvements in performance over the popular priority-based random dropping (PRD) technique.

PRE-Processing for Video Coduing with Rate-Distortion Optimization Decision

This paper proposes an adaptive video pre-processing algorithm for video coding. This algorithm works on the original image before intraor inter-prediction. It adopts Gaussian filter to remove noise and insignificant features existing in images of video. Detection and restoration of edges are followed to restore the edges which are excessively filtered out in filtered images. Rate-Distortion Optimization （RDO） is employed to decide adaptively whether a processed block or a unprocessed block is coded into bit-streams doe more efficient coding. Our experiment results show that the algorithm achieves good coding performances on both subjective and objective aspects. In addition, the proposed pre-processing algorithm is transparent to decoder, and thus can be compliant with any video coding standards without modifying the decoder.

Frame-layer bit allocation for multi-view video coding based on frame complexity estimation

Current multi-view video coding （MVC） reference model in joint video team （JVT） does not provide efficient rate control schemes. This paper presents a rate control algorithm for MVC by improving the quadratic rate-distortion （R-D） model. We reasonably allocate bit-rate among views based on the correlation analysisl The proposed algorithm consists of three levels to control the rate bits more accurately, of which the frame layer allocates bits according to the frame complexity and the temporal activity. Extensive experiments show that the proposed algorithm can control the bit rate efficiently.

Research on the Bit Computation Model for Time Variable Channel Bandwidth

For the encoding bit-rate problem in H.264 wireless video communication, this paper analyzes the bit-rate computation model and the Mean Square Error distortion model, establishes the relation between the quantization parameter of the encoding bit-rate and the intra frame refresh rate of the macro block, and put forward a new ideal about the coding rate on the basis of general binomial computation model theory. Furthermore, this method not only can adaptively adjust the bit allocation and quantization pa rameter and prevents buffer from overflowing downward or upward under given network bandwidth, but also can apply the rate-distortion to optimize the solution method, control the encoding bits accurately and optimizes the allocation between the intra and inter frame encoding macro block.

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.

Variable Bit Rate Fuzzy Control for Low Delay Video Coding

Rate control plays a critical role in achieving perceivable video quality under a variable bit rate,limited buffer sizes and low delay applications.Since a rate control system exhibits non-linear and unpredictable characteristics,it is difficult to establish a very accurate rate-distortion(R-D)model and acquire effective rate control performance.Considering the excellent control ability and low computing complexity of the fuzzy logic in non-linear systems,this paper proposes a bitrate control algorithm based on a fuzzy controller,named the Fuzzy Rate Control Algorithm(FRCA),for All-Intra(AI)and low-delay(LD)video source coding.Contributions of the proposed FRCA mainly consist of four aspects.First,fuzzy logic is adopted to minimize the deviation between the actual and the target buffer size in the hypothetical reference decoder(HRD).Second,a fast lookup table is employed in fuzzy rate control,which reduces computing cost of the control process.Third,an input domain determination scheme is proposed to improve the precision of the fuzzy controller.Fourth,a novel scene change detection is introduced and integrated in the FRCA to adaptively adjust the Group-of-Pictures(GOP)length when the source content fluctuates.The FRCA can be transplanted and implemented in various industry coders.Extensive experiments show that the FRCA has accurate variable bit-rate control ability and maintains a steady buffer size during the encoding processes.Compared with the default configuration encoding under AI and LD,the proposed FRCA can achieve the target bit rates more accurately in various classical encoders.

The partial side information problem with additional reconstructions

We consider a quadratic Gaussian distributed lossy source coding setup with an additional constraint of identical reconstructions between the encoder and the decoder.The setup consists of two correlated Gaussian sources,wherein one of them has to be reconstructed to be within some distortion constraint and match with a corresponding reconstruction at the encoder,while the other source acts as coded side information.We study the tradeoff between the rates of two encoders for a given distortion constraint on the reconstruction.An explicit characterization of this trade-off is the main result of the paper.We also give close inner and outer bounds for the discrete memoryless version of the problem.

Novel Rate-Control Algorithm Based on TM5 Framework

Rate control is a key technology in the fields of video coding and transmission, and it has attracted a great attention and has been studied extensively. The TM5 framework of MPEG-2 is a classical rate control algo-rithm and has being widely used. However, it has some underlying drawbacks during practical applications such as the poor rate control precision and high computational complexity. Hence, in this paper, a novel rate-control algorithm based on the TM5 framework is proposed. The drawback of the target bit allocation method of the original TM5 algorithm is firstly analyzed and improved. Then, a new rate-distortion model is incorporated into the rate control algorithm to implement rate prediction to enhance the rate-control precision. Meanwhile, the macro-block (MB) level rate control is adapted to be frame level to reduce the computational complexity. Experiments are conducted and some results are given. Compared with the original TM5 algo-rithm, the improved novel algorithm not only can enhance the rate-control precision but also can reduce the complexity and the fluctuation of decoded image quality.

STRONG CODING THEOREM AND ASYMPTOTICERROR EXPONENT OF ARBITRARILY VARYING SOURCE WITH A FIDELITY CRITERION

When the error probability is less than 2--"", the minimum compression rate(r-optimal rate) of arbitrarily varying source with a fidelity criterion is determined. Thisgeneralizes the result of Arutyunyan and Mekaushll] for discrete memoryless source witha fidelity criterion, and is called strong coding theorem of arbitrarily varying source witha fidelity criterion. We also determine the asymptotic error exponellt for arbitrarily varying source with a fidelity criterion. This generalizes Marton’s result in [2] for discretememoryless source with a fidelity criterion.

M3FEC:Joint Multiple Description Coding and Forward Error Correction for Interactive Multimedia in Multiple Path Transmission

The best-effort internet has inherent limitations on the end-to-end performance for interactive multimedia communications. This paper presents a multiple description coding （MDC） and forward error correction （FEC） based multiple path transmission schemes for interactive multimedia （M3FEC）, which improves the end users’ experience by maximizing a rate-distortion （R-D） based optimization problem. The proposed model considers both the network diversity and the application’s stringent requirements, and combines the individual merits of the three promising technologies of multiple path overlay routing, MDC and FEC. Extensive numerical analysis and PlanetLab experiments demonstrate that M3FEC successfully combats packet losses, error propagation, and unpredictable network dynamics. This method also significantly increases distortion for interactive multimedia by over 10 dB than traditional IP-layer single path transmission in poor network environments, and outperforms the performance achieved by using MDC or FEC alone.

A Two-Layer Real-Time Rate Control Algorithm Based on Lagrange Optimum Algorithm

Because of the iterative process in the rate control algorithm of the H.264 draft, it is not suitable for real-time transmission. A two-layer real-time rate control algorithm based on the Lagrange optimum algorithm is proposed. This algorithm consists of two layers, namely, the group of pictures （GOP） layer and the frame layer. The simulation results show that the proposed algorithm is competitive in terms of peak signal-to-noise ratio （PSNR）, real bitrates and the Q-value among frames over the original algo- rithm.

Modelling the utility of group testing for public health surveillance

In epidemic or pandemic situations,resources for testing the infection status of individuals may be scarce.Although group testing can help to significantly increase testing capabilities,the(repeated)testing of entire populations can exceed the resources of any country.We thus propose an extension of the theory of group testing that takes into account the fact that definitely specifying the infection status of each individual is impossible.Our theory builds on assigning to each individual an infection status(healthy/infected),as well as an associated cost function for erroneous assignments.This cost function is versatile,e.g.,it could take into account that false negative assignments are worse than false positive assignments and that false assignments in critical areas,such as health care workers,are more severe than in the general population.Based on this model,we study the optimal use of a limited number of tests to minimize the expected cost.More specifically,we utilize information-theoretic methods to give a lower bound on the expected cost and describe simple strategies that can significantly reduce the expected cost over currently known strategies.A detailed example is provided to illustrate our theory.