Rate-distortion Research Articles

Functional Covering of Point Processes

We introduce a new distortion measure for point processes called functional-covering distortion. It is inspired by intensity theory and is related to both the covering of point processes and logarithmic-loss distortion. We obtain the distortionrate function with feedforward under this distortion measure for a large class of point processes. For Poisson processes, the rate-distortion function is obtained under a general condition called constrained functional-covering distortion, of which both covering and functional-covering are special cases. Also for Poisson processes, we characterize the rate-distortion region for a two-encoder CEO problem and show that feedforward does not enlarge this region.

Hypergraph-Based Source Codes for Function Computation Under Maximal Distortion

This work investigates functional source coding problems with maximal distortion, motivated by approximate function computation in many modern applications. The maximal distortion treats imprecise reconstruction of a function value as good as perfect computation if it deviates less than a tolerance level, while treating reconstruction that differs by more than that level as a failure. Using a geometric understanding of the maximal distortion, we propose a hypergraph-based source coding scheme for function computation that is constructive in the sense that it gives an explicit procedure for finding optimal or good auxiliary random variables. Moreover, we find that the hypergraph-based coding scheme achieves the optimal rate-distortion function in the setting of coding for computing with side information and achieves the Berger-Tung sum-rate inner bound in the setting of distributed source coding for computing. It also achieves the El Gamal-Cover inner bound for multiple description coding for computing and is optimal for successive refinement and cascade multiple description problems for computing. Lastly, the benefit of complexity reduction of finding a forward test channel is shown for a class of Markov sources.

Texture-Aware Spherical Rotation for High Efficiency Omnidirectional Intra Video Coding

To adapt to the existing video coding standards, omnidirectional videos are usually projected from Three-Dimensional (3D) sphere to Two-Dimensional (2D) plane. However, this projection will cause geometrical stretching distortion and boundary discontinuity, which may degrade coding efficiency. In this paper, we present a Spherical Rotation based Omnidirectional Video Coding (SROVC) method, which exploits the textural properties of omnidirectional videos with spherical rotation. Firstly, SROVC framework is presented and Full-traversal Spherical Rotation (FSR) is developed to derive the optimal rotation angle with frame-level Rate Distortion Optimization (RDO). Secondly, to achieve comparable coding gains and lower computational complexity when compared with FSR, a Texture-aware Spherical Rotation (TSR) method is proposed to predict the rotation angle. Finally, to further reduce complexity and maintain coding efficiency, a Group-oriented TSR (G-TSR) approach is presented, in which the group length is statistically determined. Extensive experiments demonstrate that the proposed TSR and G-TSR schemes can achieve bit rate reductions up to 4.38%, 0.94% and 0.89% on average for CubeMap Projection (CMP) based high efficiency omnidirectional video coding. Additionally, the TSR scheme achieves bit rate saving from 0.91% to 1.19% on average under three more CMP-based projection formats, and 1.89% for joint rotation of X, Y, and Z axes.

Exploring the Benefits of Cross-Modal Coding

Multi-modal services, typically integrating such signals as audio, video, and haptic, will become an inevitable application trend of the 5G and beyond. However, due to the essential differences among the haptic and audio/video signals, the existing coding schemes usually fail to satisfy the critical requirements in terms of the rate distortion performance. Inspired by the phenomenon that hearing, sight and touch are highly correlated, we provide an affirmative answer by proposing the framework of cross-modal coding, which compresses multi-modal signals aided by their semantic correlation. In particular, the highlights of this work lie in addressing three fundamental technical problems: i) how to exploit the semantic correlation among different modalities, ii) to what extent of benefit we can get from cross-modal coding, and iii) how to design a general cross-modal codec. On the theoretical end, we determine the minimum number of bits required to compress haptic signals under the rate conditions of video streams through investigating their semantic correlation. On the technical end, we design a general cross-modal codec to approach the optimal compression limit by using the AI-enabled cross-modal prediction and channel coding. Numerical results demonstrate that the proposed cross-modal coding can achieve significant benefits relative to the existing schemes, especially when multi-modal signals have strong semantic correlation.

Export-Import Misalignment and Gross Fixed Capital Formation in Nigeria

This study examines export-import misalignment and gross fixed capital formation in Nigeria. Three variables are used and myriad of control variables, spanning the period of 1981-2020. The model was tested with several econometrics and statistical instruments. The results from the findings indicates that, export-import misalignment has a negative and an insignificant impact on gross fixed capital formation, however, export and import impact gross fixed capital formation differently. There was a 30.3% threshold evidence as indicated in the F-statistics value in the model. We recommend that, the federal government of Nigeria should reprioritize her needs. They should spend more on capital expenditures as against the current trend of 68:32% allocations to recurrent and capital expenditures respectively. Efforts must be made to mobilize the desired level of gross national savings that could be big enough to attract foreign direct investments as FDI will help to complement our domestic savings. Government should work on her potentially exportable commodities. The proceeds should be utilized in the importation of needed technical tools and components. Basic infrastructures like good roads, electricity supply and security must be seen to be adequate. This will help to reduce the drudgeries currently being faced by manufacturers. Efforts should be geared towards a reduction in exchange rate distortion, volatility and general mismanagement. Policy formulators in Nigeria need to enact some investor friendly policies that will encourage, promote and attract more capital inflows (Be it official or private inflows) and provide a conducive and enabling environment for gross fixed capital formation to thrive. There is need to play down on speculative businesses and to invest into the real sectors of the economy. There is also the need to reduce the level of capital flight out of country. Inflows should be tied to specific, relevant and purposeful projects. This will help to create employment opportunities in the long run. Prudence and proper accountability should be the watchword in the management of accruals from official capital inflows and transfers. Production of petroleum products need be increased: Since the wealth of the nation is hinged on this mono-product. Lastly, macroeconomic projections should guide the overall level of expenditure etc.

Analysis of coding unit partitioning and complexity reduction at intra-prediction mode of HEVC

ABSTRACT The emerging trend of ultra-high definition (4 K, UHD) video services necessitates coding efficiency, which is much higher than H.264. High-Efficiency Video Coding (HEVC) has been designed with the intention of drastically reducing the bit rate without compromising the video quality. The key feature is the introduction of a larger block structure with flexible block sizes from 64 × 64 to 8 × 8 samples. The enhanced feature has imposed higher complexity, specifically at the intra-prediction mode level of HEVC. The proposed research work has presented a novel hybrid approach to reducing the number of candidate Coding Units (CU) passing through irrelevant partition patterns and time-consuming iterative Rate-Distortion (RD) cost estimation process. The hybrid approach is implemented in two steps. In the first step gradient evaluation is performed and the CU is categorized as homogeneous or non-homogeneous. In the second step Early Termination is adopted by using machine learning for partitioning non-homogeneous CU. As the number of candidate Coding Units undergoing the time-consuming RD process is reduced by using the two-step hybrid approach, the proposed approach proves to be the best of its kind. The reduction in the number of candidate units undergoing the RD process reduces the complexity of the intra-prediction process. Encoding time is the measure of complexity and the proposed hybrid approach reduces the encoding time by an average of 36% compared with the HEVC reference model and also by an average of 26% as compared with the previous work. The Peak Signal to Noise Ratio (PSNR) reduced by an average of 0.1% indicates a slight reduction in the encoded image quality. The proposed approach significantly reduced the encoding time without affecting the RD performance of the encoder. The image quality is not compromised substantially when considering the improvement in the encoding time and the reduction in the complexity at an intra-prediction level.

Convolutional neural network-based optimal R-λ intra rate control in Versatile Video Coding

Since intra frame rate control has no prior information to rely on, it is difficult to obtain suitable initial rate control parameters for achieving optimal intra coding. The optimal bit allocation and λ decision are proposed based on a convolutional neural network (CNN) in R-λ intra frame rate control. First, according to the rate distortion (R-D) and rate lambda (R-λ) relationships of the coding tree unit (CTU), a CNN with four outputs is designed to predict the key parameters of R-D and R-λ curves. Second, with the R-D optimization of a frame, the coding optimization equation of the frame λ and the target bit can be built to derive the optimal CTU bit allocation. Last, a suitable CTU λ can be obtained based on the allocated bit and predicted R-λ curve for a CTU to minimize the total distortion of the current frame. The experimental results show that the proposed algorithm can improve the intra frame rate control performance by 0.31 dB with a good rate control accuracy of 4.76%.

Learning Fair Representations via Rate-Distortion Maximization

Abstract Text representations learned by machine learning models often encode undesirable demographic information of the user. Predictive models based on these representations can rely on such information, resulting in biased decisions. We present a novel debiasing technique, Fairness-aware Rate Maximization (FaRM), that removes protected information by making representations of instances belonging to the same protected attribute class uncorrelated, using the rate-distortion function. FaRM is able to debias representations with or without a target task at hand. FaRM can also be adapted to remove information about multiple protected attributes simultaneously. Empirical evaluations show that FaRM achieves state-of-the-art performance on several datasets, and learned representations leak significantly less protected attribute information against an attack by a non-linear probing network.

Calibrating the Attack to Sensitivity in Differentially Private Mechanisms

This work studies the power of adversarial attacks against machine learning algorithms that use differentially private mechanisms as their weapon. In our setting, the adversary aims to modify the content of a statistical dataset via insertion of additional data without being detected by using the differential privacy to her/his own benefit. The goal of this study is to evaluate how easy it is to detect such attacks (anomalies) when the adversary makes use of Gaussian and Laplacian perturbation using both statistical and information-theoretic tools. To this end, firstly via hypothesis testing, we characterize statistical thresholds for the adversary in various settings, which balances the privacy budget and the impact of the attack (the modification applied on the original data) in order to avoid being detected. In addition, we establish the privacy-distortion trade-off in the sense of the well-known rate-distortion function for the Gaussian mechanism by using an information-theoretic approach. Accordingly, we derive an upper bound on the variance of the attacker’s additional data as a function of the sensitivity and the original data’s second-order statistics. Lastly, we introduce a new privacy metric based on Chernoff information for anomaly detection under differential privacy as a stronger alternative for the (ϵ,δ)-differential privacy in Gaussian mechanisms. Analytical results are supported by numerical evaluations.

Analysis of the non-linear relationship between Interest Rate Distortions in China’s Shadow Banking System and short-term Capital Flows

ABSTRACT Using semiparametric generalized additive models, we studied how the market-based interest rates in China’s shadow banking system and interest rate distortion affect the country’s short-term capital flows. We used big data of shadow banking products to calculate the shadow banking interest rate and the interest rate distortion index. We improved the indirect measurement method for capital flows and used the bounds testing cointegration approach to measure China’s monthly short-term capital flows. With the calculated data, we estimated the nonlinear impact of shadow banking interest rate spreads and interest rate distortion on short-term capital flows. Results indicate that a higher shadow banking interest rate spread creates greater short-term capital inflows and the degree of interest rate distortion has a non-linear inverted U-shaped effect on short-term capital flows. Thus, the Chinese government should proceed with interest rate liberalization to eliminate the current “dual-track interest rate system” and strengthen the monitoring of cross-border capital flows so as to reduce the risks of abnormal short-term capital flows.

Compensation for farms compulsorily acquired for mining in Ghana: an analysis of the laws, compensation practices and valuation methods

PurposeThis paper aims to examine the compensation practices and the valuation methods valuers apply in the context of the current legal framework for expropriation to assess compensation for farms impacted by mining in Ghana.Design/methodology/approachCompensation reports and archival materials were examined to identify the issues related to the valuation methods, compensation practices and expropriation procedures in the mining sector. Interviews were then conducted with 35 farmers and farmers' representatives, officials of mining companies, representatives of the Land Valuation Division of the Lands Commission and valuers/researchers on the issues identified through the document analysis.FindingsThe results reveal that the lack of express standards for assessing compensation for mining-impacted crops has occasioned variations in the valuation methods and the standard crop population for compensation. The study further reveals the impacts of exchange rate distortions on crop compensation values.Practical implicationsThe study empirically substantiates the arguments for a revised compensation regime in Ghana's mining sector. Valuers, mining companies and policymakers' awareness of this research will impact farm compensation valuation practices in the future.Social implicationsThe adequacy of compensation for mining-impacted farmers remains a topical issue, especially in African countries. This research contributes to the literature and reveals the socio-economic impacts of the current compensation regime on the livelihoods of expropriated farmers.Originality/valueThis paper is the first to analyse the valuation methods, the compensation values and the key parameters valuers apply in assessing compensation for mining-impacted crops in Ghana.

Mental control of uncertainty.

Can you reduce uncertainty by thinking? Intuition suggests that this happens through the elusive process of attention: if we expend mental effort, we can increase the reliability of our sensory data. Models based on "rational inattention" formalize this idea in terms of a trade-off between the costs and benefits of attention. This paper surveys the origin of these models in economics, their connection to rate-distortion theory, and some of their recent applications to psychology and neuroscience. We also report new data from a numerosity judgment task in which we manipulate performance incentives. Consistent with rational inattention, people are able to improve performance on this task when incentivized, in part by increasing the reliability of their sensory data.

Learned image compression with generalized octave convolution and cross-resolution parameter estimation

Recently, image compression approaches based on deep learning have gradually outperformed existing image compression standards including BPG and VVC intra coding. In particular, the application of the context-adaptive entropy model significantly improves the rate-distortion (R-D) performance, in which hyperpriors and autoregressive models are jointly utilized to effectively capture the spatial redundancy of the latent representations. However, the latent representations still contain some spatial correlations. In addition, these methods based on the context-adaptive entropy model cannot be accelerated in the decoding process by parallel computing devices, e.g. FPGA or GPU. To alleviate these limitations, we propose a learned multi-resolution image compression framework, which exploits the recently developed octave convolutions to factorize the latent representations into the high-resolution (HR) and low-resolution (LR) parts, similar to wavelet transform, which further improves the R-D performance. To speed up the decoding, our scheme does not use context-adaptive entropy model. Instead, we exploit an additional hyper layer including hyper encoder and hyper decoder to further remove the spatial redundancy of the latent representation. Moreover, the cross-resolution parameter estimation (CRPE) is introduced into the proposed framework to enhance the flow of information and further improve the rate-distortion performance. An additional information-fidelity loss is proposed to the total loss function to adjust the contribution of the LR part to the final bit stream. Experimental results show that our method separately reduces the decoding time by approximately 73.35 and 93.44 % compared with that of state-of-the-art learned image compression methods, and the R-D performance is still better than H.266/VVC(4:2:0) and some learning-based methods on both PSNR and MS-SSIM metrics across a wide bit rates.

Fast mode decision for CU coding based on CNN for the VVC standard

Versatile Video Coding (VVC) is the latest generation of the video coding standard. In VVC, the advanced quadtree with a nested multitype tree (QTMT) partition structure provides more flexible coding unit (CU) partition sizes compared with the quadtree (QT) decision tree structure applied in the previous High Efficiency Video Coding (HEVC) standard. This flexibility, achieved by the new QTMT partitioning improvement, considerably improves the coding performance while increasing the coding computational complexity caused mainly by the rate distortion optimization processing. To overcome the complexity issue, a fast deep intra QTMT decision tree approach based on a convolution neural network (CNN) is adopted to determine the QTMT depth decision of each 128 × 128 Coding Tree Unit (CTU). The proposed algorithm predicts both the BT depths at 32 × 32 CUs and the QT depths at 64 × 64 using trained CNNs designed for each structure instead of processing the RDcost. Experimental results prove that the suggested deep QTMT approach achieves an important complexity reduction of up to 55.51% compared with the original reference software VTM3.0, with an average of about 35% encoding time reduction accompanied by an insignificant loss in encoding performance.

An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation

A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed. Such a source model is motivated by the recent surge of interest in the semantic aspect of information: the intrinsic state corresponds to the semantic feature of the source, which in general is not observable but can only be inferred from the extrinsic observation. There are two distortion measures, one between the intrinsic state and its reproduction, and the other between the extrinsic observation and its reproduction. Under a given code rate, the tradeoff between these two distortion measures is characterized by the rate-distortion function, which is solved via the indirect rate-distortion theory and is termed as the semantic rate-distortion function of the source. As an application of the general model and its analysis, the case of Gaussian extrinsic observation is studied, assuming a linear relationship between the intrinsic state and the extrinsic observation, under a quadratic distortion structure. The semantic rate-distortion function is shown to be the solution of a convex programming programming problem with respect to an error covariance matrix, and a reverse water-filling type of solution is provided when the model further satisfies a diagonalizability condition.

Latitude-Based Flexible Complexity Allocation for 360-Degree Video Coding

The emerging 360-degree video has a good practical prospect in video broadcasting due to its more immersive experience than traditional videos. To maintain a high fidelity with panoramic vision, 360-degree video adopts a significantly increased resolution than planar videos, which also inevitably leads to higher encoding complexity. In this paper, we propose a flexible complexity optimization method for 360-degree video coding, in which the encoder is capable of adapting to different complexity constraints in diversified broadcasting scenarios. In particular, the proposed method is realized with a latitude-based computation complexity allocation. Firstly, it investigates the Coding Unit (CU) partition variation along the latitude (i.e., vertical axis) and divides each video frame into Largest CU (LCU)-based latitude regions. Then, it formulates the complexity allocation problem from the global complexity target to all latitude regions, by considering the intraframe dependencies and Rate-Distortion (RD) models. Finally, it calculates the solution to this complexity allocation problem and further employs it in 360-degree video coding. Comprehensive experiments on the standard dataset reveal the superiority of our method, which achieves a flexible computational time reduction from 14.53% to 68.36% whilst maintaining a high visual quality of compressed videos.

Optimum Quality Control Algorithm for Versatile Video Coding

We propose a quality control method that replaces rate control in video coding and achieves quality control with the minimum bit rate. First, based on the rate–distortion model, we establish a Karush–Kuhn–Tucker equation considering the convergent properties of variational inequalities to solve the continuous convex problem. Second, combined with the rate–quality (R-Q) model, the proposed algorithm is implemented under versatile video coding (VVC) to obtain the optimal closed-form solution for the quantization parameters of a coding unit through the Lagrange multiplier method. Experiments reveal that our algorithm can achieve stable video coding quality while guaranteeing coding performance, achieving high quality control accuracy with constant quality.

Deep Music Information Dynamics Novel Framework for Reduced Neural-Network Music Representation with Applications to Midi and Audio Analysis and Improvisation

Generative musical models often comprise of multiple levels of structure, presuming that the process of composition moves between background to foreground, or between generating musical surface and some deeper and reduced representation that governs hidden or latent dimensions of music.  In this paper we are using a recently proposed framework called Deep Musical Information Dynamics (DMID) to explore information contents of deep neural models of music through rate reduction of latent representation streams, which is contrasted with hight rate information dynamics of the musical surface. This approach is partially motivated by rate-distortion theories of human cognition, providing a framework for exploring possible relations between imaginary anticipations existing in the listener's or composer's mind, and the information dynamics of the sensory (acoustic) or symbolic score data. In the paper the DMID framework is demonstrated using several experiments with symbolic (MIDI) and acoustic (spectral) music representations. We use variational encoding to learn a latent representation of the musical surface. This embedding is further reduced using a bit-allocation method into a second stream of low bit-rate encoding. The combined loss includes temporal information in terms of predictive properties for each encoding stream, and accuracy loss measured in terms of mutual information between the encoding at low rate and the high rate surface representations. For the case of counterpoint, we also study the mutual information between two voices in a musical piece at different levels of information reduction.The DMID framework allows to explore aspects of computational creativity in terms of juxtaposition of latent/imaginary surprisal aspects of deeper structure with music surprisal on the surface level, done in a manner that is quantifiable and computationally tractable. The relevant information theory modeling and analysis methods are discussed in the paper, suggesting that a trade off between compression and prediction play an important factor in the analysis and design of creative musical systems.

Bowen’s equations for upper metric mean dimension with potential

Firstly, we introduce a new notion called induced upper metric mean dimension with potential, which naturally generalises the definition of upper metric mean dimension with potential given by Tsukamoto to more general cases, then we establish variational principles for it in terms of upper and lower rate distortion dimensions and show there exists a Bowen’s equation between induced upper metric mean dimension with potential and upper metric mean dimension with potential. Secondly, we continue to introduce two new notions, called BS metric mean dimension and packing BS metric mean dimension on arbitrary subsets, to establish Bowen’s equations for Bowen upper metric mean dimension and packing upper metric mean dimension with potential on subsets. Besides, we also obtain two variational principles for BS metric mean dimension and packing BS metric mean dimension on subsets. Finally, the special interest about the Bowen upper metric mean dimension of the set of generic points of ergodic measures are also involved.

Remote monitoring and control of the 2-DoF robotic manipulators over the internet

AbstractThis article is concerned with remote monitoring and control of the 2-degrees of freedom (DoF) robotic manipulators, which have nonlinear dynamics over the packet erasure channel, which is an abstract model for communication over the Internet, WiFi, or Zigbee modules. This type of communication is subject to imperfections, such as random packet dropout and rate distortion. These imperfections cause a significant challenge for monitoring and control of robotic manipulators in the industrial environments because sensitive data, such as sensor data and control commands may not ever reach to their destination resulting in significant performance degradation. Therefore, the effects of these imperfections must be compensated. In this article, we apply two coding and control techniques previously developed for the telepresence ad teleoperation of autonomous vehicles to compensate the effects of the above communication imperfections for remote monitoring and control of the 2-DoF robotic manipulators controlled over the packet erasure channel. To achieve this goal, we design a new linear controller and a new nonlinear controller for the 2-DoF robotic manipulators over the packet erasure channel. The first technique is based on the linearization method and the second technique uses a nonlinear controller. The performances of these two techniques for remote monitoring and control of robotic manipulators are evaluated and compared with each other in this paper. We illustrate their satisfactory performances in the presence of severe communication imperfections.