Target Bit Rate Research Articles

Inter-layer correlation-based adaptive bit allocation for enhancement layer in scalable high efficiency video coding

To facilitate the diversity of network and end-user devices, bit allocation technology must be combined with scalable high efficiency video coding (SHVC) to achieve continuous bitrate variation. However, to date, the multilayer coding feature in SHVC has not been fully utilized. In this paper, a bit allocation algorithm is proposed for the enhancement layer in SHVC. The algorithm includes two parts: a bit allocation method for the initial frame and another for subsequent frames. The bit allocation method for the initial frame consists of a bit allocation factor model constructed based on investigation of the influence of the initial target bitrate on the overall coding performance. For the bit allocation of the subsequent frames, alternate GOP coding is designed and implemented to fully exploit the inter layer correlation. In addition, an adaptive frame layer bit allocation ratio model is deduced according to the rate distortion optimization theory. Experimental results show that the proposed algorithm can improve PSNR by 0.44 dB and 0.41 dB under low delay and random-access configurations, respectively, and achieve high bitrate control accuracy. The algorithm also outperforms the state-of-the-art algorithms in term of PSNR improvement.

A Rate Control Algorithm Based on Psnr-Hvs For Hevc

HEVC/H.265 is the latest video coding standard. It has a higher compression rate under the requirement of ensuring video image quality. However, the currently used video image quality evaluation indicators cannot better fit the subjective perception of the human visual system and cannot effectively allocate target bits and update parameters so that results in reduced video image coding quality. This paper proposes a CTU-level rate control algorithm based on the image quality evaluation factor PSNR-HVS which is more in line with the perception of the human visual system. According to the PSNR-HVS of the CTU as the weight, the target bit is assigned to the current frame to be encoded. Then calculate the actual bits and PSNR-HVS after encoding the current CTU to update the parameters of the CTU at the same position in the next frame. The experimental results show that compared with HM16.20, the subjective performance of the video after encoding is improved, and the image details are more delicate, BD-Rate = -4.6, BD-PSNR-HVS = 0.17, ΔT = 4.6%.

Bayer CFA Pattern Compression With JPEG XS.

While traditional image compression algorithms take a full three-component color representation of an image as input, capturing of such images is done in many applications with Bayer CFA pattern sensors that provide only a single color information per sensor element and position. In order to avoid additional complexity at the encoder side, such CFA pattern images can be compressed directly without prior conversion to a full color image. In this paper, we describe a recent activity of the JPEG committee (ISO SC 29 WG 1) to develop such a compression algorithm in the framework of JPEG XS. It turns out that it is important to understand the "development process" from CFA patterns to full color images in order to optimize the image quality of such a compression algorithm, which we will also describe shortly. We introduce (1) a novel decorrelation step upfront processing (the so-called Star-Tetrix transform), along with (2) a pre-emphasis function to improve the compression efficiency of the subsequent compression algorithm (here, JPEG XS). Our experiments clearly indicate a gain over a RGB compression workflow in terms of complexity and quality (between 1.5dB and more than 4dB depending on the target bitrate). A comparison is also made with other state-of-the-art CFA compression techniques.

Reduced Reference Perceptual Quality Model With Application to Rate Control for Video-Based Point Cloud Compression.

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0.

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.

Performance analysis of AGSM adaptive unipolar MIMO-OFDM for visible light communication

Visible light communication is a leading technology in wireless communication which provides free spectrum with high data rate. In this paper, improvement of the data rate is achieved in the adaptive transmission for U-OFDM system which works on switching of various MIMO modes. In VLC system different MIMO modes give different performance on the basis of their modulation size. The adaptive system switches between spatial modulation (SM), spatial multiplexing (SMUX), repetition code (RC) and adaptive generalized spatial modulation (AGSM) MIMO modes on the basis of received SNR and target Bit Error rate to attain high spectral efficiency (χ). The performance of adaptive generalized spatial modulation mode is analysed and compared with the three modes (RC, SM, SMUX) over 2×2, 4×4, 8×8 MIMO systems in realistic channel models. The simulation results show that significant improvement in data rate and spectral efficiency is achieved using AGSM MIMO mode in comparison to SM, RC and SMUX MIMO modes.

Context and content based scalable video adaptation for ubiquitous multimedia communication services

A novel scalable video adaptation framework is developed to achieve the aim of ensuring enhanced Rate-Distortion (R-D) performances for the ubiquitous multimedia communication services in this study. This novel framework also integrates novel contributions in its development process. As the first contribution, novel models evaluating the motion information and spatial structure characteristics of the video contents are proposed. As the second novel contribution of the developed framework, adaptation experiments are conducted by exploiting a proposed adaptation approach relying on prioritizing the spatial, temporal, and quality layer based Network Abstraction Layer Units (NALUs) of the scalability extension of the H.264/Advanced Video Coding (AVC) gradually in accordance with the video contents’ motion and structural characteristics. The proposed adaptation approach also relies on the prioritization of the quality layer based NALUs from the key and non-key frames in both symmetric and asymmetric manners while attaining the target bit rate of a communication network. In accordance with these development steps, subjective experiments are carried to assess the quality of adapted video contents having different motion information and spatial structure characteristics under different ambient illumination conditions. Using the analysis results of the subjective experiments, another significant novelty of the framework relying on developing novel prioritization schemes, which assist an encoder to assign priority indices for each NALU of the encoded video contents based on their characteristics and the ambient illumination condition of the viewing environment, is performed. The adaptation experiments conducted in the light of the developed framework prove that highly efficient R-D performances can be attained for the video contents having any characteristic and viewed under any ambient illumination condition for assisting the multimedia communication services to ensure improved user viewing perception while easing the adverse effects of the network congestion.

A Bit Allocation Method Based on Inter-View Dependency and Spatio-Temporal Correlation for Multi-View Texture Video Coding

Due to the limited storage capacity and network bandwidth, an efficient rate control (RC) algorithm becomes more and more critical for multi-view video coding (MVC). Based on inter-view dependency and spatio-temporal correlation, a novel bit allocation method for multi-view texture video coding is proposed in this article. Firstly, considering that the distortion in the base view (BV) is directly transmitted to the dependent view (DV) by inter-view skip mode, a joint multi-view RD model is built based on the inter-view dependency. Based on the proposed joint multi-view RD model, a precise power model is derived to represent the target bitrates relationship between the BV and the DV. Secondly, since the P frame in the DV (P-DV) is mainly predicted from the corresponding I frame in the BV (I-BV) by disparity compensated prediction (DCP), the constant proportional relationship between the ratio of the average bitrates of the P-DV to the corresponding I-BV and the ratio of the total bitrates of the DV to the BV is discovered. Based on this discovery, a novel linear model is developed to assign the target bitrates of the P-DV. Finally, considering the spatio-temporal correlation, a new parameter prediction method is proposed for the R-$\lambda $ model in coding tree unit (CTU) level. Extensive experimental results show that the proposed overall method outperforms other state-of-the-art algorithms in terms of RD performance.

Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding

Medical ultrasound images are inherently noised with speckle noise, which may interfere with Computer Aided Diagnostics (CAD) tasks, such as automatic segmentation. A compression and speckle de-noising method is proposed and tested on real clinical breast and fetal ultrasound images. The proposed algorithm is based on the optimization of quantization coefficients when applying Wavelet representation on the image, where the optimization is held such that a pre-defined mathematical fidelity criterion with respect to a desired de-speckled image is obtained.The proposed algorithm yields effective speckle reduction whilst preserving the edges in the images, with a reduced computational burden compared to other existing state-of-the-art methods, such as Optimal Bayesian Non-Local Means (OBNLM). In addition, the images are simultaneously compressed to a target bit-rate.The proposed algorithm is evaluated using both objective mathematical fidelity criteria (such as Structural Similarity and Edge Preserve) as well as subjective radiologists tests. The experimental results demonstrate the ability of the proposed method to achieve de-speckled images with compression ratios of approximately 30:1, whilst obtaining competitive subjective as well as objective fidelity measures with respect to the desired de-speckled images.

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.

Error correction schemes for fully correlated quantum channels protecting both quantum and classical information

We study efficient quantum error correction schemes for the fully correlated channel on an n-qubit system with error operators that assume the form \(\sigma _x^{\otimes n}\), \(\sigma _y^{\otimes n}\), \(\sigma _z^{\otimes n}\). Previous schemes are improved to facilitate implementation. In particular, when n is odd and equals \(2k+1\), we describe a quantum error correction scheme using one arbitrary qubit \(\sigma \) to protect the data state \(\rho \) in a 2k-qubit system. The encoding operation \(\sigma \otimes \rho \mapsto \Phi (\sigma \otimes \rho )\) only requires 3k CNOT gates (each with one control bit and one target bit). After the encoded state \(\Phi (\sigma \otimes \rho )\) goes through the channel, we can apply the inverse operation \(\Phi ^{-1}\) to produce \({\tilde{\sigma }} \otimes \rho \) so that a partial trace operation can recover \(\rho \). When n is even and equals \(2k+2\), we describe a hybrid quantum error correction scheme using any one of the two classical bits \(\sigma \in \{|ij{\rangle }{\langle }ij|: i, j \in \{0,1\}\}\) to protect a 2k-qubit state \(\rho \) and two classical bits. The encoding operation \(\sigma \otimes \rho \mapsto \Phi (\sigma \otimes \rho )\) can be done by \(3k+2\) CNOT gates and a single-qubit Hadamard gate. After the encoded state \(\Phi (\sigma \otimes \rho )\) goes through the channel, we can apply the inverse operation \(\Phi ^{-1}\) to produce \(\sigma \otimes \rho \) so that a perfect protection of the two classical bits \(\sigma \) and the 2k-qubit state is achieved. If one uses an arbitrary two-qubit state \(\sigma \), the same scheme will protect 2k-qubit states. The scheme was implemented using MATLAB, Mathematica, Python and the IBM’s quantum computing framework qiskit.

Optimized Rate Control Algorithm of High-Efficiency Video Coding Based on Region of Interest

Aiming at the problems that the strategy of target bit allocation at the CTU layer has deviations from the human subjective observation mechanism, and the update phase of parametric model has a higher complexity in the JCTVC-K0103 rate control algorithm of ITU-T H.265/high efficiency video coding (HEVC) standard. Optimized rate control (ORC) algorithm of ITU-T H.265/HEVC based on region of interest (ROI) is proposed. Firstly, the algorithm extracts the region of interest of video frames based on time and space domains by using the improved Itti model. Then, the weight of target bits w is recalculated based on space-time domains to improve the rate control accuracy, and the target bits are distributed based on ROI by the adaptive weight algorithm once again to make the output videos more attuned with the human visual attention mechanism. Finally, the quasi-Newton algorithm is used to update the rate distortion model, which reduces the computational complexity in the update phase of the parametric model. The experimental results show that the ORC algorithm can obtain a better subjective quality in the compressed results with less bit error compared with the other two algorithms. Meanwhile, the rate distortion performance of the ORC algorithm is better on the premise of guaranteeing rate control performance.

Per Clip Lagrangian Multiplier Optimisation for HEVC

The majority of internet traffic is video content. This drives the demand for video compression in order to deliver high quality video at low target bitrates. This paper investigates the impact of adjusting the rate distortion equation on compression performance. An constant of proportionality, k, is used to modify the Lagrange multiplier used in H.265 (HEVC). Direct optimisation methods are deployed to maximise BD-Rate improvement for a particular clip. This leads to up to 21% BD-Rate improvement for an individual clip. Furthermore we use a more realistic corpus of material provided by YouTube. The results show that direct optimisation using BD-rate as the objective function can lead to further gains in bitrate savings that are not available with previous approaches.

A perceptual rate control algorithm based on luminance adaptation for HEVC encoders

This paper proposes a rate control algorithm by selecting a proper quantization parameter (QP) based on perceptual luminance adaptation in a single-loop encoding fashion. In this paper, the proposed algorithm uses the visual characteristics of humans to adaptively decide the number of bits available for a pixel. Then, a base QP is selected based on the proposed R–λ model. The proposed rate control determines the range of QP based on the base QP and calculates the maximum and minimum bpps within that range. The optimal bpp is obtained from the bpp range by considering the visual characteristics of the human being, and the QP value is determined by the optical bpp through the proposed R–λ model. In the proposed rate control algorithm, the QP value is selected based on the R–λ model by considering the perceptual luminance adaptation model at the CTU level. The number of target bits is decided to decide the QP, subject to visual sensitivity based on JND thresholds. With the use of the proposed rate control algorithm, bits for non-noticeable regions can be saved, and the remaining bits can be consumed for perceptually noticeable regions to enhance the overall subjective quality with the similar amount of total bits. The proposed method shows lower average variances of bits and PSNR fluctuations. Also, the proposed method achieves an approximately 0.19 higher MOS value on average under DSCQS test, compared with the conventional R–λ model-based rate control algorithm.

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images.

Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

A Novel Rate Control Algorithm Based on ρ Model for Multiview High Efficiency Video Coding

Most existing rate control algorithms are based on the rate-quantization (R-Q) model. However, with video coding schemes becoming more flexible, it is very difficult to accurately model the R-Q relationship. Therefore, in this study we propose a novel ρ domain rate control algorithm for multiview high efficiency video coding (MV-HEVC). Firstly, in order to further improve the efficiency of MV-HEVC, this paper uses our previous research algorithm to optimize the MV-HEVC prediction structure. Then, we established the ρ domain rate control model based on multi-objective optimization. Finally, it used image similarity to analyze the correlation between viewpoints, using encoded information and frame complexity to proceed in bit allocation and bit rate control of the inter-view, frame lay, and base unit. The experimental simulation results show that the algorithm can simultaneously maintain high coding efficiency, where the average error of the actual bit rate and the target bit rate is only 0.9%.

A new ADCS method based on guided filter for tea HSIs

To improve the reconstructed performance of mass data storage, transmission and preserving spectral characteristics for tea hyperspectral images(HSIs), a new adaptive distributed compressive sensing method based on guided filter (ADCSGF) is proposed. According to the spectral characteristics of tea HSIs, all bands can be divided into different parts which can be further grouped with different band count. Bands of each group are compressed and reconstructed by distributed compressive sensing method, in which the adaptive bit stream allocation strategy based on the residual error is used to obtain the target bit rate for each non-key band and the key band is regarded as the guided filter band to improve the quality of reconstructed non-key bands in each group. The experimental results showed that ADCSGF can improve the subjective quality of image reconstruction and achieve at least a 1.5 dB higher peak signal-to-noise ratio (PSNR) of spectral dimension decorrelation method (SSDC) than that of distributed compressive sensing based on guided filter (DCSGF). ADCSGF can obtain better reconstructed spectral curve at the sampling rate of 0.1Bpp(Bytes per pixel) and achieve better normalized root mean square error (RMSE) performance at the sampling rate from 0.2Bpp to 0.5Bpp than those of DCSGF.

Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy

Compressing large images with a generative model goes beyond typical image encoding standards under a notably low bitrate. In this paper, we step toward practical generative compression systems based on recent advances. Specifically, we show that the channel redundancy of the latent representation produced by an autoencoder network can be effectively compressed via mask compression. The mask compression performs quantization on the channel variance of latent representation instead of original values. Instead of training multiple models, changing the mask leads to a simple and efficient variable rate compression scheme. Then, we estimate the relative bitrate by measuring the L1 norm of the channel variance and hence obtain the rate-distortion formulation. The L1 regularizer assumes a Laplacian prior on the channel variance, through which model we develop corresponding methods to produce approximate images at a target bitrate. This eliminates the need for manually searching hyperparameters for our variable-rate compression. We conduct exhaustive experiments to demonstrate the advanced performance of the proposed method in preserving image quality and semantics.

A Frame-Level Constant Bit-Rate Control Using Recursive Bayesian Estimation for Versatile Video Coding

In this paper, we present a frame-level constant bit-rate (CBR) control method using recursive Bayesian estimation (RBE) for Versatile Video Coding (VVC). An R - $\lambda $ model for rate control (RC) has handled the total texture and non-texture bits at a time and has worked reasonably well in High Efficiency Video Coding (HEVC). Nevertheless, if the rate estimation is inaccurately performed, that is, the $R$ and $\lambda $ values for a current frame cannot be linearly modeled with their respective values in the previous frames, the resulting RC performance is degraded. In our work, we adopt the RBE which alternates prediction and update steps not only to precisely estimate the rates, but also to allocate target bits based on the changes in the distortions of the previously coded frames, thus considering the rates and distortions simultaneously. Therefore, an elaborate RC can be performed especially at fluctuating frame complexities. Experimental results show that our RC method outperforms the RC of VVC Test Model (VTM-5.0) in terms of normalized root mean square error (NRMSE) with maximum (average) 34.95% (12.35%) improvement, and maintains higher visual quality consistency in terms of standard deviation of PSNR by 33.07% (22.34%) improvement for All Intra (AI), maximum (average) 44.82% (27.29%) and 22.54% (9.50%) for Low Delay (LD), and maximum (average) 47.35% (39.94%) and 30.35% (18.54%) for Random Access (RA), respectively, compared to the default RC method of the original VTM-5.0.

Rate Control for Video-based Point Cloud Compression.

Rate control is a necessary tool for video-based point cloud compression (V-PCC). However, there is no solution specified on this topic yet. In this paper, we propose the first rate control algorithm for V-PCC. Generally, a rate control algorithm is divided into two processes: bit allocation and bitrate control. In V-PCC, the total bits are composed of three parts: the header information including the auxiliary information and occupancy map, the geometry video, and the attribute video. The bit allocation aims to assign the total bits to these three parts. Since the auxiliary information and occupancy map are encoded losslessly, the bit cost of the header information is fixed. Therefore, we only need to assign bits between the geometry and attribute videos. Our first key contribution is the proposed video-level bit allocation algorithm between the geometry and attribute videos to optimize the overall reconstructed point cloud quality. Then we assign geometry and attribute video bits to each group of pictures (GOP), each frame, and each basic unit (BU). Our second key contribution is that we assign zero bits to the BUs with only unoccupied pixels. The unoccupied pixels are useless for the reconstructed quality of the point cloud and therefore should be assigned zero bits. In the bitrate control process, the encoding parameters are determined, and the model parameters are updated for each frame and BU to achieve the target bits. Our third key contribution is that we propose a BU-level model updating scheme to handle the case where various patches may be placed in different positions in neighboring frames. We use the auxiliary information to find the corresponding BU in the previous frame and apply its model parameters to the current BU. The proposed algorithms are implemented in the V-PCC and High Efficiency Video Coding (HEVC) reference software. The experimental results show that the proposed rate control algorithm can achieve significant bitrate savings compared with the state-of-the-art method.