Distortion Optimization Research Articles

Intra Frame Coding In Advanced Video Coding Standard (H.264) to Obtain Consistent PSNR and Reduce Bit Rate for Diagonal Down Left Mode Using Gaussian Pulse

Intra prediction process of H.264 video coding standard used to code first frame i.e. Intra frame of video to obtain good coding efficiency compare to previous video coding standard series. More benefit of intra frame coding is to reduce spatial pixel redundancy with in current frame, reduces computational complexity and provides better rate distortion performance. To code Intra frame it use existing process Rate Distortion Optimization (RDO) method. This method increases computational complexity, increases in bit rate and reduces picture quality so it is difficult to implement in real time applications, so the many researcher has been developed fast mode decision algorithm for coding of intra frame. The previous work carried on Intra frame coding in H.264 standard using fast decision mode intra prediction algorithm based on different techniques was achieved increased in bit rate, degradation of picture quality(PSNR) for different quantization parameters. Many previous approaches of fast mode decision algorithms on intra frame coding achieved only reduction of computational complexity or it save encoding time and limitation was increase in bit rate with loss of quality of picture. In order to avoid increase in bit rate and loss of picture quality a better approach was developed. In this paper developed a better approach i.e. Gaussian pulse for Intra frame coding using diagonal down left intra prediction mode to achieve higher coding efficiency in terms of PSNR and bitrate. In proposed method Gaussian pulse is multiplied with each 4x4 frequency domain coefficients of 4x4 sub macro block of macro block of current frame before quantization process. Multiplication of Gaussian pulse for each 4x4 integer transformed coefficients at macro block levels scales the information of the coefficients in a reversible manner. The resulting signal would turn abstract. Frequency samples are abstract in a known and controllable manner without intermixing of coefficients, it avoids picture getting bad hit for higher values of quantization parameters. The proposed work was implemented using MATLAB and JM 18.6 reference software. The proposed work measure the performance parameters PSNR, bit rate and compression of intra frame of yuv video sequences in QCIF resolution under different values of quantization parameter with Gaussian value for diagonal down left intra prediction mode. The simulation results of proposed algorithm are tabulated and compared with previous algorithm i.e. Tian et al method. The proposed algorithm achieved reduced in bit rate averagely 30.98% and maintain consistent picture quality for QCIF sequences compared to previous algorithm i.e. Tian et al method.

Review of Fast Mode Decision Algorithms for Intra Prediction in High Efficiency Video Coding Standard

The emerging High Efficiency Video Coding (HEVC) standard is a new improved next generation video coding standard. HEVC aims to provide improved compression performance as compared to all other video coding standards. To improve the coding efficiency a number of new techniques have been used. The higher compression efficiency is obtained at the cost of an increase in the computational load. In HEVC 35 modes are provided for intra prediction to improve the compression efficiency. The best mode is selected by Rate Distortion Optimization (RDO) process. It achieves significant improvement in coding efficiency compared with previous standards. However, this causes high encoding complexity. This paper discuss the various fast mode decision algorithms for intra prediction in HEVC.

Terminal embeddings

In this paper we study terminal embeddings, in which one is given a finite metric (X,dX) (or a graph G=(V,E)) and a subset K⊆X of its points are designated as terminals. The objective is to embed the metric into a normed space, while approximately preserving all distances among pairs that contain a terminal. We devise such embeddings in various settings, and conclude that even though we have to preserve ≈|K|⋅|X| pairs, the distortion depends only on |K|, rather than on |X|.We also strengthen this notion, and consider embeddings that approximately preserve the distances between all pairs, but provide improved distortion for pairs containing a terminal. Surprisingly, we show that such embeddings exist in many settings, and have optimal distortion bounds both with respect to X×X and with respect to K×X.Moreover, our embeddings have implications to the areas of Approximation and Online Algorithms. In particular, [10] devised an O˜(log⁡r)-approximation algorithm for sparsest-cut instances with r demands. Building on their framework, we provide an O˜(log⁡|K|)- approximation for sparsest-cut instances in which each demand is incident on one of the vertices of K (aka, terminals). Since |K|≤r, our bound generalizes that of [10].

Optimality in Distortion Control in Reversible Watermarking Using Genetic Algorithms

This paper proposes a reversible contrast mapping (RCM)-based reversible watermarking (RW) algorithm (RCM-RW) where optimal distortion thresholds adaptive to the image characteristics are used. A generalized form of RCM is developed using a set of transformation functions; each one may be considered as a point on a straight line, called here as operating point. Each operating point offers trade-off benefits on embedding distortion, data hiding capacity and security for the hidden data. The choice of an operating point is governed by adaptive distortion control thresholds, the values depend on the partitioning of the images on smooth, texture and edge regions. However, region-specific optimal distortion threshold is difficult to represent in closed-form expression. Genetic algorithms (GAs), due to their complex searching nature, are used here for calculating the set of distortion thresholds. Simulation results show that high embedding capacity, improved security and imperceptibility of the hidden information can be met simultaneously using a particular combination of such operating points on optimal basis. Extensive simulation results show that the proposed method outperforms the existing RW techniques.

Temporally Dependent Rate-Distortion Optimization for Low-Delay Hierarchical Video Coding.

Low-delay hierarchical coding structure (LD-HCS), as one of the most important components in the latest High Efficiency Video Coding (HEVC) standard, greatly improves coding performance. It groups consecutive P/B frames into different layers and encodes them with different quantization parameters (QPs) and reference mechanisms in such a way that temporal dependency among frames can be exploited. However, due to varying characteristics of video contents, temporal dependency among coding units differs significantly from each other in the same or different layers, while a fixed LD-HCS scheme cannot take full advantage of the dependency, leading to a substantial loss in coding performance. This paper addresses the temporally dependent rate distortion optimization (RDO) problem by attempting to exploit varying temporal dependency of different units. First, the temporal relationship of different frames under the LD-HCS is examined, and hierarchical temporal propagation chains are constructed to represent the temporal dependency among coding units in different frames. Then, a hierarchical temporally dependent RDO scheme is developed specifically for the LD-HCS based on a source distortion propagation model. Experimental results show that our proposed scheme can achieve 2.5% and 2.3% BD-rate gain in average compared with the HEVC codec under the same configuration of P and B frames, respectively, with a negligible increase in encoding time. Furthermore, coupled with QP adaption, our proposed method can achieve higher coding gains, e.g., with multi-QP optimization, about 5.4% and 5.0% BD-rate saving in average over the HEVC codec under the same setting of P and B frames, respectively.

An efficient Lagrangian multiplier selection method based on temporal dependency for rate–distortion optimization in H.265/HEVC

Rate–distortion optimization is a crucial instrument to achieve the best performance of information lossy video coding based on rate–distortion theory, and it is generally realized by Lagrange optimization using a Lagrangian multiplier. An efficient Lagrangian multiplier selection method considering temporal dependency (LMS-TD for short) is introduced in this paper for the random-access (RA) configuration in H.265/HEVC (High Efficiency Video Coding). The temporal relationship of distortion and bit-rate is analyzed for videos coded using an H.265/HEVC compliant coder (HM-14.0) with the RA configuration, based on which an optimized set of Lagrangian multipliers is determined. The LMS-TD method achieves an average BD-rate (Bjontegaard bit-rate) reduction of 4.59%. Picture quality evaluation was also performed using an objective perceptual video quality measurement technique, i.e., the Structural Similarity Index (SSIM), where the LMS-TD method demonstrated superior performance in video quality.

Fast CU size and prediction mode decision algorithm for HEVC based on direction variance

As the latest video coding standard, High Efficiency Video Coding (HEVC) achieves high coding performance, up to 50% bit rate savings compared with the previous standard, H.264/Advanced Video Coding while maintaining the same output video quality. Intra-coding in the HEVC significantly improves the compression efficiency, due to the various kinds of coding unit (CU) sizes, and high density of angular prediction modes. However, the improvement of the coding performance is obtained at the expense of the extraordinary computation complexity, which obstructs the usefulness of HEVC encoder for real-time applications. This paper presents a novel intra-encoding algorithm for HEVC, which is composed of an efficient CU partitioning technique and a fast intra-mode decision method based on direction variance. We utilize a preprocessing method in which the texture complexity and direction energy distribution along a special direction are extracted from a macroblock. According to the texture complexity obtained, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower-dimensions CUs or not. Furthermore, a subset of prediction modes in accordance with the dominant direction energy distribution is chosen for the further rough mode decision and rate–distortion optimization process. The simulation results indicate the algorithm based on image understanding achieves better trade-off between rate–distortion performance and complexity reduction than the previous algorithms. Compared with the reference software HM16.7, the proposed algorithm can save 57% coding time on average, with a negligible bit rate increase of 0.65%, and quality losses lower 0.08 dB, respectively.

Distortion Optimisation through Welding Simulation

Distortion Optimisation through Welding Simulation

Vote Until Two of You Agree: Mechanisms with Small Distortion and Sample Complexity

To design social choice mechanisms with desirable utility properties, normative properties, and low sample complexity, we propose a new randomized mechanism called 2-Agree. This mechanism asks random voters for their top alternatives until at least two voters agree, at which point it selects that alternative as the winner. We prove that, despite its simplicity and low sample complexity, 2-Agree achieves almost optimal distortion on a metric space when the number of alternatives is not large, and satisfies anonymity, neutrality, ex-post Pareto efficiency, very strong SD-participation, and is approximately truthful. We further show that 2-Agree works well for larger number of alternatives with decisive agents.

Towards an FPGA-Based HEVC Encoder: A Low-Complexity Rate Distortion Scheme for AMVP

Advanced motion vector prediction (AMVP) is a new technique adopted in the latest high efficiency video coding (HEVC) standard. AMVP block predicts an initial motion vector of the current block from a given set of candidates by means of rate distortion (RD) optimization process. Due to the large number of different-sized blocks, simplification of RD optimization process in AMVP block is highly appreciated. Therefore, we present a new RD optimization technique for AMVP block in HEVC encoder. The proposed RD calculation approach finds the best AMVP candidate by processing less number of feature pixels per every block. Experimental results show notable speedup in terms of AMVP processing time with tolerable quality degradation (PSNR) and bitrate requirement. The proposed RD calculation technique reduces the RD computational complexity of the AMVP block by 87.5% as maximum (i.e. 1.7% of the whole encoder complexity). This improvement is accompanied with a modest average PSNR loss of 0.10 dB and an increase by 2.4% in terms of bitrate. On the other hand, we present an FPGA-based architecture for AMVP unit in HEVC encoder. The proposed architecture was prototyped, simulated and synthesized on Xilinx Virtex-7 XC7VX550T FPGA. At 188 MHz clock frequency, the proposed architecture processes 8 K (7680 \(\times \) 4320) YCrCb resolution at 60 fps while utilizing less than 1% of the FPGA resources.

Distortion Control and Optimization for Lossy Embedded Compression in Video Codec System

Distortion Control and Optimization for Lossy Embedded Compression in Video Codec System

An Effective Rate Distortion Optimization Method for Reliability HEVC Systems

An Effective Rate Distortion Optimization Method for Reliability HEVC Systems

DVFS-aware motion estimation design scheme based on bandwidth–rate–distortion optimization in application processor systems

In this paper, a novel dynamic voltage–frequency scaling-aware (DVFS-aware) bandwidth- efficient motion estimation (ME) scheme is presented for mobile application processor (AP) systems. Under volatile operating performance conditions due to the power management mechanism, we model the coding bandwidth (BW) and coding performance for the video processor as a convex function of the working frequency. In this paper, we present a bandwidth–rate–distortion (B–R–D) optimized framework that will guarantee the smallest possible rate–distortion cost among coding BW constraints applied in video coding design. By formulating the coding bandwidth-constrained ME problem as an optimization problem, known convex optimization theory can be applied to yield optimal resource-constrained compression. Using varied CIF (352×288)- and HP (1280×720)-sized video sequences with different motion activities over our proposed DVFS-aware video coding approach, the excellent results in terms of coding performance and coding bandwidth savings are obtained. With negligible quality loss, the proposed scheme under coding BW constraints achieves 45–65% coding BW usage reduction over HD-sized 30 frame/s video coding.

Constant Distortion Embeddings of Symmetric Diversities

Abstract Diversities are like metric spaces, except that every finite subset, instead of just every pair of points, is assigned a value. Just as there is a theory of minimal distortion embeddings of fiite metric spaces into L1, there is a similar, yet undeveloped, theory for embedding finite diversities into the diversity analogue of L1 spaces. In the metric case, it iswell known that an n-point metric space can be embedded into L1 withO(log n) distortion. For diversities, the optimal distortion is unknown. Here, we establish the surprising result that symmetric diversities, those in which the diversity (value) assigned to a set depends only on its cardinality, can be embedded in L1 with constant distortion.

On Reconstructing Linear Combinations of Correlated Gaussian Sources Over a MAC

In this letter, we study the joint source-channel coding problem of communicating the linear functions of correlated Gaussian sources over a Gaussian multiple access channel. In this setup, each transmitter observes one of two correlated sources. The receiver aims to reconstruct a linear combination of both correlated Gaussian sources within an average distortion level. For this setup, we first obtain a new lower bound on the achievable distortion and then, we propose a novel lattice-based hybrid transmission scheme and its achievable distortion is derived. We show that our proposed scheme can achieve the optimal distortion under some conditions.

Quantization parameter cascading for video coding: leveraging a new temporal distortion propagation model

In video coder, inter-frame prediction causes distortion propagation among temporally adjacent frames. This distortion dependency is a crucial factor for rate control optimization. Quantization parameter cascading (QPC) is an efficient technique to achieve dependent rate distortion optimization (RDO). This paper proposes a general framework for temporal dependency analysis by leveraging a distortion propagation model, which is derived by employing window-based preanalysis on original frames. Then, a quantization parameter offset \(\delta \) model is proposed for achieving fine-granularity quantization control, according to the amount of distortion propagation measured by the relative propagation cost \(\rho \). This paper applies competitive decision in exploring \(\delta \)–\(\rho \) model as accurate as possible and then proposes an improved \(\delta \)–\(\rho \) model tailored for dependent RDO. The simulation results verify that the temporal QPC algorithm with the proposed model achieves up to 1.1–1.4 dB PSNR improvement, with smaller temporal distortion fluctuation contributed by efficient bit allocation.

Adaptive Progressive Motion Vector Resolution Selection Based on Rate-Distortion Optimization.

In the state-of-the-art H.265/HEVC video coding standard, the motion vector (MV) resolution is fixed to be 1/4-pel for the entire video sequence, while the inherent video characteristics, e.g., texture complexities and motion activities, have been largely ignored. Obviously such strategy may not suffice the demand of high-accuracy motion compensation. In this paper, we propose a specially designed rate-distortion model in terms of the MV resolution by taking the video characteristics into consideration. In particular, the MV resolution selection is formulated as a rate-distortion optimization problem by analyzing the rate-distortion cost of each MV resolution candidate. To further improve the coding performance, the progressive MV resolution strategy is employed, where the optimal progressive MV resolution is determined by decision trees constructed with the rate-distortion model. In this manner, a novel adaptive progressive motion vector resolution selection scheme can be realized and the MV resolution can be adaptively adjusted based on the properties of local content. Extensive experiments and comparisons show that the proposed algorithm significantly improves the coding performance, and 1.8% BD-rate gain on average has been achieved without introducing any noticeable computational complexity.

Rate Control via Adjustment of Lagrange Multiplier for Video Coding

In this paper, we propose to improve the performance of the rate-control mechanism in video encoders by adaptively adjusting the Lagrange multiplier $\lambda $ based on the stochastic subgradient method. Using this $\lambda $ value for each coding unit, the quantization parameter (QP) is sought in the process of rate–distortion optimization (RDO). A log-linear model can be utilized to predict the most probable QP and accordingly narrow the search range. The generated bit number is used to update the $\lambda $ value for the next coding unit. The extensive experimental results show that the proposed rate-control mechanism significantly enhances the rate-distortion performance compared with the H.264/AVC (Advanced Video Coding) reference software.

HEVC 인트라 코딩을 위한 모멘트 기반 고속 CU크기 결정 방법

HEVC 비디오 압축 표준은 기존 비디오 표준보다 더 다양한 블록 구조와 예측 모드를 사용함으로써 우수한 부호화 성능을 제공하나, 최적의 블록 크기 및 예측 모드를 결정하기 위한 RDO(Rate Distortion Optimization)과정으로 인해 연산량이 많다는 단점을 가진다. 이를 개선하기 위해 본 논문에서는 화면 내 예측 수행 전 CU영역의 모멘트 값을 계산하고 이를 CU영역의 텍스쳐 복잡도로 이용하여 CU의 분할 여부를 결정하는 모멘트 기반의 고속 CU크기 결정 방법을 제안한다. 제안하는 방법은 기존의 방법을 차용하여 CU영역의 밝기 값에 대한 분산 값을 계산하여 영역의 텍스쳐 평평도를 추정하고, 추가로 CU영역의 밝기 값에 대한 비대칭도를 계산하여 CU영역을 이루는 밝기 값 분포의 비대칭성 정도를 측정한 뒤 이를 조합하여 기존 방법보다 더 정밀하게 텍스쳐 복잡도를 측정하였으며, 이를 RDO과정 중 현재 CU의 분할 여부를 결정하는데 이용하여 기존의 부정확한 CU분할 여부 결정 방법을 개선시킨 고속 CU크기 결정 방법을 제안한다. 제안 방법의 실험 결과는 기존 방법 대비 4.2%의 BD-rate 감소를 보여주며, HM-10.0과 비교하여 BD-rate는 1.1% 증가하였고, 인코딩 시간이 32% 절감되었다. The High Efficiency Video Coding (HEVC) standard provides superior coding efficiency by utilizing highly flexible block structure and more diverse coding modes. However, rate-distortion optimization (RDO) process for the decision of optimal block size and prediction mode requires excessive computational complexity. To alleviate the computation load, this paper proposes a new moment-based fast CU size decision algorithm for intra coding in HEVC. In the proposed method, moment values are computed in each CU block to estimate the texture complexity of the block from which the decision on an additional CU splitting procedure is performed. Unlike conventional methods which are mostly variance-based approaches, the proposed method incorporates the third-order moments of the CU block in the design of the fast CU size decision algorithm, which enables an elaborate classification of CU types and thus improves the RD-performance of the fast algorithm. Experimental results show that the proposed method saves 32% encoding time with 1.1% increase of BD-rate compared to HM-10.0, and 4.2% decrease of BD-rate compared to the conventional variance-based fast algorithm.

Power Distortion Optimization for Uncoded Linear Transformed Transmission of Images and Videos.

Recently, there is a resurgence of interest in uncoded transmission for wireless visual communication. While conventional coded systems suffer from cliff effect as the channel condition varies dynamically, uncoded linear-transformed transmission (ULT) provides elegant quality degradation for wide channel SNR range. ULT skips non-linear operations, such as quantization and entropy coding. Instead, it utilizes linear decorrelation transform and linear scaling power allocation to achieve optimized transmission. This paper presents a theoretical analysis for power-distortion optimization of ULT. In addition to the observation in our previous work that a decorrelation transform can bring significant performance gain, this paper reveals that exploiting the energy diversity in transformed signal is the key to achieve the full potential of decorrelation transform. In particular, we investigated the efficiency of ULT with exact or inexact signal statistics, highlighting the impact of signal energy modeling accuracy. Based on that, we further proposed two practical energy modeling schemes for ULT of visual signals. Experimental results show that the proposed schemes improve the quality of reconstructed images by 3~5 dB, while reducing the signal modeling overhead from hundreds or thousands of meta data to only a few meta data. The perceptual quality of reconstruction is significantly improved.