Optimal Quantization Parameter Research Articles

A Rate Control Scheme for VVC Intercoding Using a Linear Model

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination.

Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding

High-Efficiency Video Coding (HEVC) is one of the most widely studied coding standards. It still uses the block-based hybrid coding framework of Advanced Video Coding (AVC), and compared to AVC, it can double the compression ratio while maintaining the same quality of reconstructed video. The quantization module is an important module in video coding. In the process of quantization, quantization parameter is an important factor in determining the bitrate in video coding, especially in the case of limited channel bandwidth. It is particularly important to select a reasonable quantization parameter to make the bitrate as close as possible to the target bitrate. Aiming at the problem of unreasonable selection of quantization parameters in codecs, this paper proposes using a differential evolution algorithm to assign quantization parameter values to the coding tree unit (CTU) in each frame of 360-degree panoramic video based on HEVC so as to strike a balance between bitrate and distortion. Firstly, the number of CTU rows in a 360-degree panoramic video frame is considered as the dimension of the optimization problem. Then, a trial vector is obtained by randomly selecting the vectors in the population for mutation and crossover. In the mutation step, the algorithm generates a new parameter vector by adding the weighted difference between two population vectors to a third vector. And the elements in the new parameter vector are selected according to the crossover rate. Finally, the trial vector is regarded as the quantization parameter of each CTU in the CTU row to encode, and the vector with the least rate distortion is selected. The algorithm will produce the optimal quantization parameter combination for the current video. The experimental results show that compared to the benchmark algorithm of HEVC reference software HM-16.20, the proposed algorithm can provide a bit saving of 1.86%, while the peak signal-to-noise ratio (PSNR) can be improved by 0.07 dB.

Rate distortion optimization with adaptive content modeling for random-access versatile video coding

In this paper, we study the capability of improving the rate-distortion (RD) performance based on the adaptive content modeling in the Versatile Video Coding (VVC) standard. In particular, the frame-level dependent relationships and inherent RD relationship are explored. As such, the rate dependency, distortion dependency and inherent RD characteristics are fully utilized in the global rate distortion optimization (RDO) process, and the quantization parameter (QP) for each frame could be adaptively solved. To facilitate the adaptive QP calculation, a two-pass coding strategy is proposed. In the first-pass coding, the video statistics are sufficiently collected with the proposed Dual Motion Compensation and Residual Coding (DMCRC) method to generate the parameters for the content-aware models. During the second-pass coding, the optimal QP at the frame level is obtained by optimizing the global RD performance with the dependent and inherent models. The proposed algorithm is implemented on VVC test model (VTM-4.0) and achieves significant performance gain for test sequences with constant and varying scenes.

Smart algorithm in wireless networks for video streaming based on adaptive quantization

SummaryNowadays, smart multimedia network services have become crucial in the healthcare system. The network parameters of Quality of Service (QoS) are widely affecting the efficiency and accuracy of multimedia streaming in wireless environments. This paper proposes an adaptation framework model, which makes a relation between the QP (quantization parameter) in H.264 and H.265 codecs and the QoS of 5G wireless technology. Besides, the effect of QP and packet loss have been studied because of their impact on video streaming. Packet loss of 5G wireless network characteristic is emulated to determine the impact of QP on the received video quality using objective and subjective quality metrics such as PSNR (peak signal to noise ratio), SSIM (structure similarity), and DMOS (differential mean opinion score). In this research, a Testbed is implemented to stream the encoded video from the server to the end users. The application model framework has automatically evaluated the QoE (Quality of Experience). Accordingly, the model detects the defect of network packet loss and selects the optimum QP value to enhance the QoE by the end‐users. The application has been tested on low and high video motions with full high definition (HD) resolution (1920 × 1080) which were taken from ( https://www.xiph.org/downloads/). Test results based on the objective and subjective quality measurements indicate that an optimal QP = 35 and QP = 30 have been chosen for low and high motion respectively to satisfy user QoE requirements.

Rate Control for Predictive Transform Screen Content Video Coding Based on RANSAC

In predictive transform video coding, optimal bit allocation and quantization parameter (QP) estimation are important to control the bit rate of blocks, frames and the whole sequence. Common solutions to this problem rely on trained models to approximate the rate-distortion (R-D) characteristics of the video content during coding. Moreover, these solutions are mainly targeted for natural content sequences, whose characteristics differ greatly from those of screen content (SC) sequences. In this article, we depart from such trained R-D models and propose a low-complexity RC method for SC sequences that leverages the availability of information about the R-D characteristics of previously coded blocks within a frame. Namely, our method first allocates bits at the frame- and block-levels based on their motion and texture characteristics. It then approximates the R-D and R-QP curves of each block by a set control points and random sample consensus (RANSAC). Finally, it computes the appropriate block-level QP values to attain a target bit rate with the minimum distortion possible. The proposed RC method is embedded into a standard High-Efficiency Video Coding (H.265/HEVC) encoder and evaluated on several SC sequences. Our results show that our method not only attains better R-D performance than that of H.265/HEVC and other methods designed for SC sequences but also attains a more constant and higher reconstruction quality on all frames.

Scene-level two-pass video rate controller for H.265/HEVC standard

A scene-level two-pass video rate controller for high efficiency video coding (HEVC) standard is proposed in this paper. The proposed rate controller is suitable for many pre-recorded applications in which encoded videos are transferred later on through a communication channel with limited bandwidth and buffering constraint. This rate controller operates in two passes. In the first pass, the video sequence is compressed and relevant information is collected. In the second pass, an optimal base quantization parameter (QP) for each video scene is calculated by simplifying the λ-domain rate-distortion model. Then, using the optimal base QP and the known QP cascading technique, a QP is computed for each picture in the scene. In this way, the quality of the compressed video is kept relatively constant during a scene and thus it provides a high-level perceptual quality for the compressed video. The base QP may be slightly modified during the second pass encoding by a fuzzy controller according to the second pass encoding results and the buffer status. The proposed rate controller has been implemented on H.265/HEVC standard reference software. Experimental results show that in comparison with the constant QP encoding and three known rate control algorithms the proposed algorithm provides a high-level quality for tested video sequences while the required buffering delay is low and the available bandwidth is fully used.

Rate Control Method Based on Deep Reinforcement Learning for Dynamic Video Sequences in HEVC

Rate control (RC) plays a critical role in the transmission of high-quality video data under certain bandwidth restrictions in High Efficiency Video Coding (HEVC). Most current HEVC RC algorithms based on spatio-temporal information for rate-distortion (R-D) model parameters cannot effectively handle the cases with dynamic video sequences that contain fast moving objects, significant object occlusion or scene changes. In this paper, we propose an RC method based on deep reinforcement learning (DRL) for dynamic video sequences in HEVC to improve the coding efficiency. First, the rate control problem is formulated as a Markov decision process (MDP) problem. Second, with the MDP model, we develop a DRL-based algorithm to find the optimal quantization parameters (QPs) by training a deep neural network. The resulting intelligent agent selects the optimal RC strategy to reduce distortion, buffer and quality fluctuations by observing the current state of the encoder. The asynchronous advantage actor-critic (A3C) method is used to solve the MDP problem. Finally, the proposed DRL-based RC method is implemented in the newest video coding standard. Experimental results show that the proposed method offers substantially enhanced RC accuracy and consistently outperforms HEVC reference software and other state-of-the-art algorithms.

CNN-Based Fast HEVC Quantization Parameter Mode Decision

With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process. The optimal quantization parameter is calculated by comparing the rate distortion cost. In this paper, we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network, which saves video’s encoding time.

A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain.

Conventional quantization-based watermarking may be easily estimated by averaging on a set of watermarked signals via uniform quantization approach. Moreover, the conventional quantization-based method neglects the visual perceptual characteristics of the host signal; thus, the perceptible distortions would be introduced in some parts of host signal. In this paper, inspired by the Watson’s entropy masking model and logarithmic quantization index modulation (LQIM), a logarithmic quantization-based image watermarking method is developed by using the wavelet transform. Furthermore, the novel method improves the robustness of watermarking based on a logarithmic quantization strategy, which embeds the watermark data into the image blocks with high entropy value. The main significance of this work is that the trade-off between invisibility and robustness is simply addressed by using the logarithmic quantizaiton approach, which applies the entropy masking model and distortion-compensated scheme to develop a watermark embedding method. In this manner, the optimal quantization parameter obtained by minimizing the quantization distortion function effectively controls the watermark strength. In terms of watermark decoding, we model the wavelet coefficients of image by the generalized Gaussian distribution (GGD) and calculate the bit error probability of proposed method. Performance of the proposed method is analyzed and verified by simulation on real images. Experimental results demonstrate that the proposed method has the advantages of imperceptibility and strong robustness against attacks covering JPEG compression, additive white Gaussian noise (AWGN), Gaussian filtering, Salt&Peppers noise, scaling and rotation attack, etc.

Adaptive Gradient Information and BFGS Based Inter Frame Rate Control for High Efficiency Video Coding

In order to meet the emerging demands of high-fidelity video services, a new video coding standard — High Efficiency Video Coding (HEVC) is developed to improve the compression performance of high definition (HD) videos and save half of the bitrate for the same perceptual video quality compared with H.264/Advanced Video Coding (AVC). Rate control still plays a significant role in HD video data transmission via the communication channel. However, R-lambda model based HEVC rate control algorithm does not take the relationship between the encoding complexity and Human Visual System (HVS) into account, what’s more, the convergence speed of Least Mean Square (LMS) algorithm is slow. In this paper, an adaptive gradient information and Broyden Fletcher Goldfarb Shanno (BFGS) based R-lambda model (GBRL) is proposed for the inter frame rate control, where the gradient based on Sobel operator can effectively measure the frame-content complexity and BFGS algorithm converges speedily than LMS algorithm. Experimental results show that the proposed GBRL method can achieve bitrate error reduction and peak signal to noise ratio (PSNR) improvement especially for the sequences with large motion, compared to the state-of-the-art rate control methods. In addition, if the optimal initial quantization parameter (QP) prediction model based on linear regression can be incorporated into the proposed GBRL method, the performance of rate control can be further improved.

H.264/AVC를 위한 적응적 인트라 프레임 압축

인트라 프레임은 GOP (Group of Pictures)의 처음 프레임을 의미하며, H.264 표준에서 인트라 프레임의 모든 매크로블록은 하나의 양자화 파라미터로 압축한다. 그리고 인트라 프레임은 이후 프레임들을 압축할 때 사용하기 때문에 인트라 프레임의 압축 결과는 전체 프레임 그룹의 화질에 영향을 미친다. 따라서 인트라 프레임을 최적으로 압축할 수 있는 양자화 파라미터를 찾는 것은 화질 개선을 위해 중요한 요소이다. 본 논문에서는 실시간으로 인트라 프레임을 압축하기 위한 최적의 양자화 파라미터를 탐색하는 방법을 제안한다. 제안하는 알고리즘은 최적 양자화 파라미터의 특성에 대한 분석을 바탕으로 경사하강법을 이용하여 실시간으로 최적값을 탐색한다. 실험결과는 제안하는 방법이 최적 양자화 파라미터의 특성을 잘 반영하고 최적값도 정확하게 예측함을 보여준다. In H.264 standard, an intra frame is the first frame of a GOP (Group of Pictures) and all macroblocks of an intra frame are encoded using the same quantization parameter. In addition, an intra frame is used for encoding the following frames of the same GOP so the encoding results of an intra frame affect the encoding results of the entire GOP. Thus, it is important to find the optimal quantization parameter of an intra frame for improving the quality of a GOP. In this paper, we propose an searching method for an optimal quantization parameter of an intra frame in real time. The proposed method uses a gradient descent method to find the optimal value based on characteristics of the optimal quantization parameters. Experimental results show that the proposed method captures the characteristics of the optimal quantization parameter and accurately estimates the optimal value.

Optimizing rate allocation between multiview videos and associated depth maps with quantization-based virtual view distortion model and genetic algorithm

In three-dimensional video coding (3-DVC), it is reasonable to allocate different coding bits for multiview videos and associated depth maps because of their different characteristics to meet the bits’ restraints of bandwidth/storage. We first propose a virtual view average distortion model. Then, based on the quantization parameter (QP), the average distortion-QP models and sum bitrate-QP models are proposed to depict the average distortions and sum bitrates of the referenced views for multiview videos and depth maps. Finally, the 3-DVC bit allocation problem is converted as a constrained optimization problem, which is solved by a genetic algorithm to search for the optimal QP pair. Experimental results demonstrate the effectiveness of our proposed models. Since the bit allocation scheme takes the performance of synthesized view and bitrate utilization into consideration, the absolute difference between the constraint and the actual coding bitrates (referred to as “rate inaccuracy”) of the proposed method is only 7.405% on average, which greatly outperforms the fixed 5 ∶ 1 ratio-based method with a rate inaccuracy of 19.103% and the planar model-based method with a rate inaccuracy of 20.556%. Compared with these two methods, our proposed method can achieve a maximum 1.951-dB gain under the same bitrates constraint.

A Simple and High Performing Rate Control Initialization Method for H.264 AVC Coding Based on Motion Vector Map and Spatial Complexity at Low Bitrate

The temporal complexity of video sequences can be characterized by motion vector map which consists of motion vectors of each macroblock (MB). In order to obtain the optimal initial QP (quantization parameter) for the various video sequences which have different spatial and temporal complexities, this paper proposes a simple and high performance initial QP determining method based on motion vector map and temporal complexity to decide an initial QP in given target bit rate. The proposed algorithm produces the reconstructed video sequences with outstanding and stable quality. For any video sequences, the initial QP can be easily determined from matrices by target bit rate and mapped spatial complexity using proposed mapping method. Experimental results show that the proposed algorithm can show more outstanding objective and subjective performance than other conventional determining methods.

Optimum Quantization for Binary Hypothesis Testing

In this letter, optimum quantization of samples for use in ultra-wideband (UWB) ranging and communication systems is considered. The necessary conditions which the optimal quantization parameters, i.e. the quantization levels and thresholds, should satisfy are derived based on two optimality criteria of maximizing the detection probability and minimizing the error probability respectively. In particular, the design of optimal quantizers for weak signals is specially explored. To give a concrete example, applications to the case of detecting known signals in additive noise are investigated. Numerical results on four-level quantization demonstrate that the optimal quantizers perform better than suboptimal quantizers thanks to the extra information contained in the optimal parameters.

Spatial Characteristic Based Rate Control Initialization Method for Conversational Video Communication of H.264/AVC Using Linear Prediction

In this paper, we propose a method of initializing the quantization parameter ( QP ) in H.264 rate control algorithm based on spatial characteristic of conversational video. Primarily, we choose the fourteen various conversational videos and calculate the complexity of the macroblock ( MB ) using variance of MB . The spatial characteristic of each video can be quantized by the proportion of the number of complex MB in the first frame. And then we find their optimal initial QP s which can guarantee that video sequences have consistent quality by minimizing the variation of QP s in a GOP , while ensuring the minimizing actual encoding bit rate closer to the target bit rate over various low bit rates. And then we propose the fitted models of optimal initial QP s against spatial characteristic over different low bit rates. The proposed model can be used to determine optimal initial QP to guarantee consistent quality and to meet target bit rate simultaneously. The objective and subjective simulation results show that the proposed method achieves better balance quality than JM10.2 and previous research result

An Enhanced Rate Control Initialization Method for H.264 based on Image Quality Balance of GOP Using Support Vector Machines

In this paper, we propose an enhanced method of initializing the quantization parameter ( QP ) in H.264 rate control algorithm based on image quality balance of GOP (Group of Pictures) using support vector machines ( SVM s). Primarily, we analyze characteristics of some typical video sequences by motion vectors. And then we choose four video sequences with different characteristics and computer their spatial and temporal complexities and the optimal initial QP s which can guarantee to generate video sequences with consistent quality by minimizing the variation of QP s in a GOP , while ensuring the actual encoding bit rate closer to the target bit rate in various bit rates by proposed selection method of optimal initial QP . We utilize SVM s that can detect the optimal parameters by genetic algorithm to train the regression function of the extracted features that are the spatial and temporal complexities as well as given the target bit rates of sample video sequences and target that is optimal initial QP s. When any video sequence is given under any target bit rate, its spatial complexity is calculated and mapped to one of four samples through the proposed mapping method. Finally, its optimal initial QP is determined by regression function according to the mapped spatial complexity, corresponding temporal complexity of sample video sequence and given target bit rate

Optimal Frame Rate Allocation and Quantizer Selection for Unicast and Multicast Wireless Scalable Video Communication

In this paper, we present novel schemes for video quality maximization in the context of H.264 scalable video coding-based 4G wireless broadcast and multicast video transmission to heterogeneous multimedia wireless clients. In the first part, we address the issue of optimal frame rate allocation when the multimedia server is constrained by the processing overheads required for tasks such as video encoding, bit-stream extraction, packetization, etc. We demonstrate that the above problem of perceptual quality maximization is well represented by a constrained optimization framework. Based on the system model, we present a closed-form solution for frame rate allocation and a comprehensive algorithm for sum quality maximization. In the second part, we propose a novel rate partitioning-based optimal scalable video (RPSV) transmission framework. A typical wireless multicast group comprises of multimedia clients with varying wireless link qualities. Conventional multicast video transmission is constrained by the broadcast/ multicast group (BMG) subscriber with the worst link capacity. Hence, we propose the RPSV scheme for optimal partitioning of the multicast group for transmission of the H.264-coded base and enhancement scalable video layers. We demonstrate that the optimal wireless link quality-based BMG partition and the associated quantization, time-fraction parameters can be computed by solving a series of convex objective minimization problems and derive a closed-form solution for the convex problem at each step. We compare the results obtained using the above mentioned optimal frame rate allocation and quantization parameter selection schemes with the results obtained using conventional schemes and demonstrate the superiority of the proposed algorithms in both unicast and multicast scenarios.

Efficient HD video coding with joint first-order-residual (FOR) and second-order-residual (SOR) coding technique

A new video coding algorithm called the first-order-residual/second-order-residual (FOR/SOR) codec is proposed for high definition (HD) video coding in this work. Several advanced coding techniques are adopted in the proposed FOR/SOR codec. For the FOR codec, the well known block-based motion compensated predictive codec is used to exploit temporal and spatial correlations in input image frames. However, it is observed that there still exists structured residual signal after the FOR coding, and a SOR coder is developed to encode residual image frames efficiently. To improve the coding performance furthermore, we consider bit allocation between the FOR and SOR coders at the same block and determine their optimal quantization parameters systematically. It is shown by experimental results that the proposed FOR/SOR codec outperforms H.264/AVC significantly in HD video coding.

PSNR-based Initial QP Determination for Low Bit Rate Video Coding

In H.264/AVC, the first frame of a group of pictures (GOP) is encoded in intra mode which generates a large number of bits. The number of bits for the I-frame affects the qualities of the following frames of a GOP since they are encoded using the bits remaining among the bits allocated to the GOP. In addition, the first frame is used for the inter mode encoding of the following frames. Thus, the initial quantization parameter (QP) affects the following frames as well as the first frame. In this paper, an adaptive peak signal to noise ratio (PSNR)-based initial QP determination algorithm is presented. In the proposed algorithm, a novel linear model is established based on the observation of the relation between the initial QPs and PSNRs of frames. Using the linear model and PSNR results of the encoded GOPs, the proposed algorithm accurately estimates the optimal initial QP which maximizes the PSNR of the current GOP. It is shown by experimental results that the proposed algorithm predicts the optimal initial QP accurately and thus achieves better PSNR performance than that of the existing algorithm.

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Rate control is a critical issue in H.264/AVC video coding standard because it suffers from some shortcomings that make the bit allocation process not optimal. This leads to a video quality that may vary significantly from frame to frame. Our aim is to enhance the rate control efficiency in H.264/AVC baseline profile by handling two of its defects: the initial quantization parameter (QP) estimation for Intra-Frames (I-Frames) and the target number of bits determination for Inter-Frames (P-Frames) encoding. First, we propose a Rate-Quantization (R-Q) model for the I-Frame constructed empirically after extensive experiments. The optimal initial QP calculation is based on both target bit-rate and I-Frame complexity. The I-Frame target bit-rate is derived from the global target bit-rate by using a new non-linear model. Secondly, we propose an enhancement of the bit allocation process by exploiting frame complexity measures. The target number of bits determination for P-Frames is adjusted by combining two temporal measures: the first is a motion ratio based on actual bits used to encode previous frames; the second measure exploits the difference between two consecutive frames and the histogram of this difference. The simulation results, carried out using the JM15.0 reference software and the JVT-O016 rate control algorithm, show that the right choice of initial QP for I-Frame and first P-Frame allows improvement of both the bit-rate and peak signal-to-noise ratio (PSNR). Finally, the Inter-Frame bit allocation process further improves the bit-rates while keeping the same PSNR improvement (up to +1.33 dB/+2 dB for QCIF/CIF resolutions). Moreover, this process reduces the buffer level variation leading to a more consistent quality of reconstructed videos.