Enhanced MPEG G-PCC: Addressing Challenges in the OBUF Entropy Coding Framework

Hardware implementation of HEVC CABAC binarization/de-binarization

The rate-distortion (R-D) optimized mode decision is very important to obtaining full benefit from various powerful coding tools in the most recent video coding standard, namely the high efficiency video coding (HEVC). In deciding a certain coding mode, the R-D optimality criterion takes into account both distortion and bit rate. The bit-rate is generally measured from the number of bits in the bitstream that context-adaptive binary arithmetic coding (CABAC) generates through performing a series of binarization, context modeling, interval subdivision, renormalization, outstanding bit handling, and context updating. Its simplification is important to reduce the high complexity of actual bit rate estimation by CABAC. This paper proposes the efficient estimation of output bits by counting only the number of generated binarized output of the binarization process in CABAC. The proposed method only executes binarization, without performing context modeling, interval subdivision renormalization, outstanding bit handling, or context updating. We evaluate the performances of the proposed method using HEVC reference software, and our experimental results verify that the proposed scheme is very effective in reducing the complexity of rate calculation. Compared to the HEVC reference software that performs all the CABAC execution steps, the proposed method shows an average saving in rate calculation time for all intra, low delay, and random access conditions of 55.29%, 41.05%, and 42.96%, respectively.

H.264/AVC offers critical advantages over other video compression schemes at the price of increased computational complexity. The efficiency of hardware video encoders depends on all modules embedded in the processing path. This paper presents the architecture of the H.264/AVC binary coder, which is the last stage of the video coder. The module conforms to H.264/AVC High Profile and supports two binary coding modes: context adaptive binary arithmetic coding (CABAC) and context adaptive variable-length coding (CAVLC). The architecture saves a considerable amount of hardware resources since two coding modes share the same logic and storage elements. Five versions of the arithmetic coding path are developed to study the area/performance tradeoff related to parallel symbol encoding. The implementation results show that the parallel symbol encoding allows higher efficiency. The whole architecture of the binary coder is described in VHDL and synthesized for different configurations to show the implementation cost of some coding options. For both CAVLC and CABAC modes, the architecture achieves the similar throughput able to support HDTV in real time.

In this paper, we propose a selective encryption scheme with a chaotic encryption system for the High Efficiency Video Coding (HEVC) standard in which Context Adaptive Binary Arithmetic Coding (CABAC) is the only entropy coder for transform coefficient coding. Our method focuses on the binstrings of truncated rice with a context “p” (TRp) code suffix and kth order Exp-Golomb (k = p + 1) code suffix before Binary Arithmetic Coding (BAC) for the remaining absolute level coding, which is coded by the bypass mode in the entropy coding stage. The probability of symbols does not change and CABAC decoding has no effect after encryption. Several different YUV sequences are used for experimental evaluation of the proposed algorithm. Compared to previous researches, our approach has good protection for video information, which keeps a constant bitrate and format compatibility, and meantime it has a negligible impact on encoding performance.

Context-based adaptive binary arithmetic coding (CABAC) is a single entropy coding mode of the newest video coding standard, High Efficiency Video Coding (HEVC). Despite the high coding efficiency of CABAC, its data dependencies prevent effective parallelization, which also means limited throughput. Accordingly, during the HEVC standardization activity of entropy coding, both coding efficiency and throughput were considered. This paper highlights the key techniques that were proposed and adopted up to HEVC Draft International Standard (DIS). In addition, a new method is proposed for high throughput entropy coding. This method reduces the worst-case complexity of entropy coding without significant coding efficiency loss. From the experimental results, in terms of throughput improvement, we confirm that the proposed method reduces the number of context-coded bins up to 42.1 and 15.9 % on average.

Ultra High Definition Television (UHDTV) imposes extremely high throughput requirement on video codec's based on High Efficiency Video Coding (H.265/HEVC). Context-based adaptive binary arithmetic coding (CABAC) is specified as the single operation mode for entropy coding in HEVC. Parallel and pipeline processing can be used to increase the throughput for higher performance and decrease the path delay. This paper proposes a pipeline and parallel CABAC decoder architecture adaptive to HEVC syntax elements, In order to reduce the critical path delay, we stored the RangeLps LUT and LZ LUT in the same memory and we exploit different techniques of optimization. The implementation can process 1,5 bins/cycle when operate at 133,31 MHz and improved high throughput of 200Mbin/s for parallel decoder and process 1.49 bins/cycle when operate at 134.2 MHz with throughput of 200Mbin/s for pipeline decoder. The critical path delay is optimized compared to the serial process and the architecture is coded using VHDL language, simulated and synthesized using Xilinx tools with virtex4 xc4vsx25-12ff668 card.

This paper presents a hardware design of context-based adaptive binary arithmetic coding (CABAC) for the emerging High efficiency video coding (HEVC) standard. While aiming at higher compression efficiency, the CABAC in HEVC also invests a lot of effort in the pursuit of parallelism and reducing hardware cost. Simulation results show that our design processes 1.18 bins per cycle on average. It can work at 357 MHz with 48.940K gates targeting 0.13 μm CMOS process. This processing rate can support real-time encoding for all sequences under common test conditions of HEVC standard conforming to the main profile level 6.1 of main tier or main profile level 5.1 of high tier.

The advanced video codec of H.264/AVC has been extensively employed for data compression for efficient video storage or transmissions. However, its intensive computational complexity causes a bottleneck for its real-time applications. Simultaneously, the sequential operation in the normal encoding process is not favorable with regard to hardware implementations. In this paper, a high-speed and accurate bit-rate estimation scheme is proposed to skip the actual entropy engine of context-adaptive binary arithmetic coding (CABAC), which is useful for reducing the computational complexity and saving the encoding time in H.264/AVC rate distortion (RD) optimization for mode decision. The characteristics of syntax elements and the principle of CABAC are firstly analyzed. Then guided by the spirit of parallel processing, a novel algorithm is constructed to estimate the bit-rate in RD mode decision. The proposed method significantly accelerates the encoding process while well maintaining the video quality, and is well suited to hardware implementations due to its feature of parallel processing1.

We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the best anchor of the latest geometry-based point cloud compression (G-PCC) standard that was proposed by the Moving Picture Experts Group (MPEG), the proposed method can achieve an average Bjøntegaard delta bitrate of -24.58% for the Y component (resp. -21.23% for YUV components) on the MPEG Category Solid dataset and -22.48% for the Y component (resp. -17.19% for YUV components) on the MPEG Category Dense dataset. This is the first instance that a learning-based attribute codec outperforms the G-PCC standard on these datasets by following the common test conditions specified by MPEG. Our source code will be made publicly available on https://github.com/sduxlmao/SPAC.

In this paper, we present a comparative performance analysis of low power hardware architecture for real time implementation of context adaptive binary arithmetic coding algorithm and Context-based adaptive Variable Length Coding algorithm (CAVLC) used in H.264/ MPEG part 10 video coding standards. The video compression efficiency achieved in H.264 standard is not a result of any single feature but rather a combination of a number of encoding tools. CAVLC requires considerably more processing power to decode. The encoding process is simulated by MATLAB which compress the input video sequence and ensures better performance in video compression. The results that are obtained reveal better performance of both entropy coding methods.

This paper investigates the effect of bit errors on H.264 decompression when Context-based Adaptive Binary Arithmetic Coding (CABAC) entropy coding is used. A set of simulations examines how often a single bit error in an H.264 slice causes a semantic error to be detected. This is called the Error Detection Probability (EDP). A high EDP implies a high certainty that, when no error is detected, there are no bit errors in the slice. A second set of simulations examines how long it takes for a semantic error to be detected when a bit error occurs. This measure is called the Error Detection Delay (EDD). The results of the simulations show the probability of the EDD as a function of bit distance. A third set of simulations examines the effect of undetected bit errors on decompression. The results show that undetected bit errors do not cause noticeable video quality degradation. The results of the three simulations may be used in an H.264 transmission scheme that uses semantic information as an indication of slice correctness.

In this paper, a high throughput context-based adaptive binary arithmetic coding decoder design is proposed. This decoder employs a syntax element prediction method to solve pipeline hazard problems. It also uses a new hybrid memory two-symbol parallel decoding in order to enhance performance as well as to reduce costs. The critical path delay of the two-symbol binary arithmetic decoding engine is improved by 28% with an efficient mathematical transform. Experimental results show that the throughput of our proposed design can reach 485.76 Mbins/s in the high bit-rate coding and 446.2 Mbins/s on average at 264MHz operating frequency, which is sufficient to support H.264/AVC level 5.1 real-time decoding.

A Python wrapper for Context-based Adaptive Binary Arithmetic Coding (CABAC), extracted from the Test Model (VTM) for Versatile Video Coding (VVC), is presented. Besides providing Python access to CABAC, two extensions are proposed: the probability estimation progress for each context can be traced, and sequences of integer values can be coded without designing a dedicated context model set.

Joint network-source video coding (JNSC) is targeted to achieve the optimum delivery of a video source to a number of destinations over network with capacity constraints. In this paper, a practical scalable multiple description coding is proposed for JNSC, based on Lagrangian rate allocation and scalable video coding. After the spatiotemporal wavelet transformation of input video sequence and the bit plane coding and context-based adaptive binary arithmetic coding, jointing network-source coding is performed on the coding passes of the code blocks (CB) using Lagrangian rate allocation.

Nowadays, the use of real time video communication is growing with a rapid pace. For example, Search and Rescue (SAR) applications like earthquake rescue, avalanche victims, wildfire monitoring in highway and surveillance are some of the applications of real time video communication. Most of these video communications require secure transmission of signals. For real time video communication, the processing time is considered as the most important metric to be optimized. Thus, finding a simple and time efficient encryption technique for securing the transmitted data becomes mandatory. The motive behind this work is to design a system that can process the video signal in real time providing a secure communication. In this paper, we present an efficient encryption technique which has low computation complexity. The proposed technique is based on Context-Adaptive Binary Arithmetic Coding (CABAC) where the bin-string of Intra-Prediction Mode, Motion Vector Difference (MVD) and residue coefficients are encrypted with the random behavior of chaotic systems. The random bit streams used for encryption are generated with chaotic systems using Logistic map. The experimental results show that the proposed technique is very effective for real-time applications and robust against different types of attacks.