Yongfei Zhang

Gradient-based fast decision for intra prediction in HEVC

As the next generation standard of video coding, the High Efficiency Video Coding(HEVC) achieves significantly better coding efficiency than all existing video coding standards, which is however at the cost of a much higher computation complexity. To address this issue, this paper presents a gradient-based fast decision algorithm for intra prediction in HEVC. More specifically, the intra prediction in HEVC is divided into two stages: prediction unit(PU) size decision and mode decision. At the PU size decision process, four orientation features are extracted from the coding unit by the intensity gradient filters to decide the texture complexity and texture direction of the coding unit, and then the texture direction is used to exclude impossible prediction modes at the mode decision process. Compared to HEVC reference software, the proposed algorithm saves around 56.7% of the encoding time in intra high efficiency setting and up to 70.86% in intra low complexity setting with slight performance degradation.

Fast Coding Unit Depth Decision Algorithm for Interframe Coding in HEVC

As the next generation standard of video coding, the High Efficiency Video Coding (HEVC) achieves significantly better coding efficiency than all existing video coding standards. A Coding Unit (CU) quad tree concept is introduced to HEVC to improve the coding efficiency. Each CU node in quad tree will be traversed by depth first search process to find the best Coding Tree Unit (CTU) partition. Although this quad tree search process can obtain the best CTU partition, it is very time consuming, especially in interframe coding. To alleviate the encoder computation load in interframe coding, a fast CU depth decision method is proposed by reducing the depth search range. Based on the depth information correlation between spatio-temporal adjacent CTUs and the current CTU, some depths can be adaptively excluded from the depth search process in advance. Experimental results show that the proposed scheme provides almost 30% encoder time savings on average compared to the default encoding scheme in HM8.0 with only 0.38% bit rate increment in coding performance.

Fine-Granularity Transmission Distortion Modeling for Video Packet Scheduling Over Mesh Networks

Packet scheduling is a critical component in multi-session video streaming over mesh networks. Different video packets have different levels of contribution to the overall video presentation quality at the receiver side. In this work, we develop a fine-granularity transmission distortion model for the encoder to predict the quality degradation of decoded videos caused by lost video packets. Based on this packet-level transmission distortion model, we propose a content-and-deadline-aware scheduling (CDAS) scheme for multi-session video streaming over multi-hop mesh networks, where content priority, queuing delays, and dynamic network transmission conditions are jointly considered for each video packet. Our extensive experimental results demonstrate that the proposed transmission distortion model and the CDAS scheme significantly improve the performance of multi-session video streaming over mesh networks.

Multihop Packet Delay Bound Violation Modeling for Resource Allocation in Video Streaming Over Mesh Networks

Resource allocation plays a critical role in multisession video streaming over mesh networks to maximize the overall video presentation quality under transmission delay and network resource constraints. A critical component in efficient resource allocation is to analyze and model the multihop queuing behavior along the transmission path, estimate the packet loss ratio due to delay bound violation, and predict the amount of video quality degradation after multihop video transmission. In this work, we develop a multihop packet delay bound violation model to predict the packet loss probability and end-to-end distortion for video streaming over multihop networks. To this end, we extract salient features to characterize the input source and network conditions of links along the transmission path and construct a learning-based model using artificial neural network (ANN). Based on this model, we then formulate the resource allocation into a nonconvex optimization problem which aims to minimize the overall video distortion while maintaining fairness between sessions. We solve this optimization problem using Lagrangian duality methods. Extensive experimental results demonstrate that, with this widely-used offline-training-online-estimation mechanism, the proposed model is potentially applicable to almost all network conditions and can provide fairly accurate estimation results as compared with other models with a given sample data set. The proposed optimization algorithm achieves more efficient resource allocation than existing schemes.

Region-classification-based rate control for flicker suppression of I-frames in HEVC

In High Efficiency Video Coding (HEVC), the coding efficiency of I-frames is lower than P-frames and B-frames, which will cause the flicker artifact, especially in low bitrates applications. We propose a region-classification-based rate control for Coding Tree Units (CTUs) in I-frames to improve the reconstructed quality of I-frames to suppress the flicker artifact. The CTUs in I-frame are classified into three regions according to their motion vectors and complexity. When the bit budget of one I-frame is used up, the target bitrates for the remaining CTUs will be adjusted according to the regions they belong to, and the pixel-based unified rate-quantization (URQ) model is then used to calculate the QPs. Experimental results demonstrate that the proposed scheme can efficiently suppress the flicker artifacts and improve both the subjective and objective video quality when compared with the original scheme in HM9.0.

Transmission Distortion-optimized Unequal Loss Protection for video transmission over packet erasure channels

In this paper, we study the problem of Transmission Distortion-optimized Unequal Loss Protection (TD-ULP) under rate constraints for non-scalable video transmission over packet erasure channels. Based on a packet-level transmission distortion modeling scheme, we estimate the amount of contribution of each video packet to the reconstructed video quality, which defines the priority level of each packet. Unequal amounts of protections are then allocated to different video packets according to their priority levels as well as the dynamic channel conditions. The optimal ULP resource allocation is formulated as a constrained nonlinear optimization problem. An evolutionary algorithm based on Particle Swarm Optimization (PSO) is developed to obtain the optimal resource allocation. Our extensive experimental results demonstrate the effectiveness of the proposed TD-ULP scheme, which outperforms existing methods by up to 2dB gain in reconstructed video quality. 1*

Rate-distortion optimized unequal loss protection for video transmission over packet erasure channels

Video transmission over networks often suffers from packet loss due to network congestions and stringent end-to-end delay constraints. In this paper, we develop a Rate-Distortion optimized Unequal Loss Protection (RD-ULP) scheme to combat packet loss. Based on packet-level transmission distortion modeling, we estimate the amount of contribution of each video packet to the reconstructed video quality, which defines the priority level of each packet. Unequal amounts of protection are then allocated to different video packets according to their priority levels and the dynamic channel conditions. The proposed RD-ULP resource allocation problem is formulated as a constrained nonlinear optimization problem. An optimization algorithm based on Particle Swarm Optimization (PSO) is then developed to solve the optimal resource allocation problem. Our extensive experimental results demonstrate the effectiveness of the proposed RD-ULP scheme, which outperforms existing methods by up to 2dB in the reconstructed video quality.

Adaptive Fast Mode Decision for HEVC Intra Coding

The latest High Efficiency Video Coding (HEVC) standard offers higher performance than existing video coding standards - up to 50% bit-rate reduction at the equal perceptual quality, but with a significant encoder complexity increase. With regard to intra prediction, a set of 35 intra prediction modes is defined in HEVC to enhance the intra coding performance. However, the high complexity makes it difficult to be applied in real-time applications. To reduce the complexity of intra prediction while maintaining the coding performance, an adaptive fast mode decision algorithm for HEVC intra coding is proposed in this paper, which can efficiently reduce the number of the candidate modes for rate-distortion optimization (RDO) and thus the intra coding time. First, the relation observed between the costs of two candidate modes will be exploited to improve the efficiency of prediction. Second, both the texture consistency of neighborhood and the texture characteristic in current predict unit (PU) will also be considered. Experimental results demonstrate that the proposed algorithm saves 30.12% intra encoding time on average without incurring noticeable performance degradation and outperforms the state-of-the-art fast intra mode decision algorithms by achieving a better RD performance with approximate encoding time saving.

Quality Prediction of DWT-Based Compression for Remote Sensing Image Using Multiscale and Multilevel Differences Assessment Metric

Accurate assessment and prediction of visual quality are of fundamental importance to lossy compression of remote sensing image, since it is not only a basic indicator of coding performance, but also an important guide to optimize the coding procedure. In the paper, a novel quality prediction model based on multiscale and multilevel distortion (MSMLD) assessment metric is preferred for DWT-based coding of remote sensing image. Firstly, we propose an image quality assessment metric named MSMLD, which assesses quality by calculating distortions in three levels and multiscale sampling between original images and compressed images. The MSMLD method not only has a better consistency with subjective perception values, but also shows the distortion features and visual quality of compressed image well. Secondly, some significant characteristics in spatial and wavelet domain that link well with quality criteria of MSMLD are chosen with multiple linear regression and used to establish a compression quality prediction model of MSMLD. Finally, the quality prediction model is extended to a wider range of compression ratios from 4 : 1 to 20 : 1 and tested with experiment. The experimental results show that the prediction accuracy of the proposed model is up to 98.33%, and its mean prediction error is less than state-of-the-art methods.

Motion Classification-Based Fast Motion Estimation for High-Efficiency Video Coding

High efficiency video coding (HEVC), the latest video coding standard, is becoming popular due to its excellent coding performance. However, the significant gain in performance is achieved at the cost of substantially higher encoding complexity than its precedent H.264/AVC, in which motion estimation (ME) is the most time-consuming module that effectively removes temporal redundancy. Test zone search (TZS) is adopted as the default fast ME method in the reference software of HEVC; however, its computational complexity is still too high for real-time applications. Several fast ME algorithms have been recently proposed to further reduce ME complexity; however, these approaches typically lead to non-negligible performance loss. To address this problem, this paper proposes a motion classification-based fast ME algorithm. By exploring the motion relationship of neighboring blocks and the coding cost characteristic, the prediction unit (PU) is first categorized into one of three classes, namely, motion-smooth PU, motion-medium PU and motion-complex PU. Then different search strategies are carefully designed for PUs of each class according to their respective motion and content characteristics. Furthermore, a fast search priority-based partial internal termination scheme is presented to rapidly skip impossible positions that speeds up cost computation during the ME process. Extensive experimental results demonstrate that the proposed algorithm achieves as much as 12.47% and 20.25% reductions in total encoder complexity when compared with TZS under low delay P and random access configuration, respectively, with negligible rate-distortion degradation; thus, it outperforms state-of-the-art fast ME algorithms in terms of both coding performance and complexity reduction.