Fuzzy Holoentropy-Based Adaptive Inter-Prediction Mode Selection for H.264 Video Coding

Video communication requires sufficient resources to obtain the desired image quality. Availability of bandwidth and screen resolution from different devices on the client end, as well as compression techniques used for transmission also significantly influence to the video quality. Even though the H.264/AVC video compression standard and its scalable extension is known as Scalable Video Coding (SVC) was introduced and successfully addressed the challenge, some weaknesses persist especially for high-resolution video transmissions. The size of the bitstream generated by the SVC standard for ultra-high resolution video compression can only be met if transmitted on a network with a large bandwidth. The Scalable High-Efficiency Video Coding (SHVC) were introduced to overcome problems in the SVC standards. The new video compression standard is proven to reduce bandwidth requirements by up to 46.41 % compared to the SVC standards for the same video quality. This study introduces the performance analysis of SHVC standard video transmission as compared to SVC standard. Both compression standards are transmitted and emulated through network emulator called Mininet-Wifi to get the real transmission picture. The client-server video transmission scheme with different bandwidth and various client position are set in the experiment scenario. It is found that transmission time, packet loss, and video reconstruction quality on scalable video transmission with SHVC standard is better than scalable video transmissions with SVC standard.

Current digital video applications require delivery of digital video content to different clients over heterogeneous networks. These clients may have different system resources and bandwidth capabilities. Thus, delivering of digital video data compressed at specific spatial resolution, quality levels, frame rates and encoding those video data at specific bit rates according to the client resources and the available network is essential. The characteristics of current video transmission system pose many problems for video delivery and scalable video coding is a highly attractive solution. This thesis investigates the existing video coding standards, especially the newest H.264/advanced video coding and its scalable extension: scalable video coding. In this thesis, we address two issues related to the state-of-the-art scalable video coding: 1) computational complexity reduction; and 2) video coding efficiency enhancement. In the first part of this thesis, we address the complexity issue of spatial, signal-to-noise ratio (SNR), temporal and combined scalable video coding. Our goal is to find a good tradeoff between computational complexity and video coding efficiency. We first establish the need to reduce computational complexity for current H.264/advanced video coding and scalable video coding framework. We then evaluate the mode distribution correlations between the base layer and its enhancement layers for different video scalabilities. After the exhaustive search over all possible block partitions is performed at the base layer, the number of candidate modes for luma and chroma blocks in a macroblock that take part in rate distortion optimization calculation at enhancement layers could be greatly reduced based on the correlations. Finally, adaptive fast mode decision schemes for spatial, SNR and temporal scalable video coding are presented. Our schemes could achieve consistent and significant computational complexity reduction with negligible loss in objective and subjective video quality and insignificant increments in bit rate consumption. The second part of this thesis is to provide a solution for the problem of high computational complexity of scalable video decoders. An adaptive encoding algorithm is proposed to reduce decoder complexity in coarse grain SNR scalable video coding.

Scalable video coding offers a mechanism to deal with inter-operability among various heterogeneous devices, adaptability of video quality over varying bandwidths and backward compatibility with various pre-existing devices. Scalability is broadly classified along three major attributes as of spatial, temporal and quality, thereby enabling multiple features along these video attributes. The optimal usage of such scalability is achieved if we can couple the selection of enhancement layers with the identity of the video or various contents present in the video. This field of content-based scalability mode selection has not been exhaustively explored in the literature. In this paper, we present a simple but effective approach for intelligent selection of scalability mode. A 3-D DFT based approach is used for this purpose. Our work has been restricted to the availability of three options-spatial, temporal and spatio-temporal. The results show effectiveness of the method proposed for mode selection among these chosen options.

Multidimensional Signal, Image, and Video Processing and Coding

CHAPTER 14 - Video Coding Technology

Carrier aggregation is one of the most distinct features of 4G systems including LTE-A (Long Term Evolution Advanced), which is being standardized in 3GPP as part of LTE Release 10. The closed-loop concept with codebook based precoding MIMO (multiple-input multiple-output) and transmission mode selection, termed as RI (rank indicator) selection, have been deployed in LTE-A systems to provide high system performance required by 4G systems. This article focuses on the study of adaptive transmission mode selection in LTE-A carrier aggregation scenarios. We propose an adaptive transmission mode selection scheme using average channel capacity and BER to maximize the average spectral efficiency (ASE) with a given target bit error ratio (BER). Numerical results demonstrate improvement over each fixed modern terms of average spectral efficiency.

Error-resilient scalable compression based on distributed video coding

This paper investigates how Scalable Video Coding (SVC) can benefit from different Forward Error Correction (FEC) and transmission schemes in mobile broadcast systems. Simulation are performed in DVB-H (Digital Video Broadcasting - Handheld) systems. In DVB-H, a differentiation in robustness for the different SVC layers can be achieved at the link layer using intra-burst MPE-FEC (multi-Protocol Encapsulation FEC). The paper evaluates the gain that can be achieved with the MPE-FEC using equal (EEP) and unequal error protection (UEP), and the performance improvements of an SVC layer-aware FEC (LA-FEC) approach that can be implemented in DVB-H either at the link layer with MPE-iFEC (inter-burst MPE-FEC) or at the application layer with AL-FEC. LA-FEC improves the SVC base layer robustness generating parity information across existing dependencies within the SVC video coding structure. Laboratory measurement results using a TU6 channel model show that the performance of an SVC service in DVB-H can be significantly increased by a proper link layer FEC scheme. It is shown that using SVC in combination with LA-FEC and a proper transmission scheduling does not only give a better performance in terms of PSNR and amount of video outages compared to a single layer service at the same service bit rate, but also gives an additional lower quality layer which can be used for applications like conditional access.

In this paper, we first propose a new parallel video coding framework, considering three important factors: parallel strategy, computational complexity and task scheduling. Then combining the characteristics of scalable video coding (SVC), a novel parallel encoding structure for temporal and quality scalabilities is introduced to obtain a high speedup of parallel SVC. Since the data dependencies in SVC are complex and time variant, the scheduling of parallel SVC is extremely difficult. In order to find the optimal scheduling solution, directed acyclic graph (DAG) is exploited to model the dependencies of encoding tasks, and the complexities of the encoding tasks which are accurately estimated by the Kalman filter to weight the scheduling tasks. Finally, two heuristic scheduling algorithms are also proposed to achieve a high encoding speed of parallel SVC. Experimental results show that the speedup of our parallel method was higher (about 60%) than previous work. Using the proposed method, high definition (HD) SVC videos can be encoded in real time.

The rapid increasing demands of wireless multimedia applications have boosted the developments of video delivery technologies with cross-layer designs, driven by optimizing quality of experiences (QoEs) of end users. In this paper, a near optimal power allocation scheme, targeting at maximizing QoE, is proposed for transmitting scalable video coding (SVC) based videos over multi-input multi-output (MIMO) systems. Both transmission errors in the physical (PHY) layer and video source coding characteristics in the application (APP) layer are jointly considered in the proposed scheme. A near optimal solution is achieved by decomposing the original optimization problem into several convex optimization sub-problems. Detailed algorithms with corresponding theoretical reasoning are provided. Since forward error corrections (FEC) techniques are widely implemented in modern wireless communication systems, the proposed scheme is further extended to the systems with Reed-Solomon (RS) code and a more practical approach with different modulation and coding schemes (MCSs). The near optimality of our proposed scheme, in terms of measured utilities, is shown by comparing with the exhaustive searched optimal solutions. Simulations with real H.264 SVC video traces demonstrate the effectiveness of our proposed scheme by comparing with other existing schemes in terms of well-accepted video quality assessment methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.

This is the second part of a two-part paper considering bidirectional relay networks with half-duplex nodes and block fading where the nodes transmit with a fixed transmission rate. In Part I, it was shown that a considerable gain in terms of sum throughput can be obtained by optimally selecting the transmission modes or, equivalently, the states of the nodes, i.e., the transmit, the receive, and the silent states, based on the qualities of the involved links. To enable adaptive transmission mode selection, the relay has to be equipped with two buffers for storage of the data received from the two users. The protocol proposed in Part I was delay unconstrained and provides an upper bound for the performance of practical delay-constrained protocols. In this paper, we propose two heuristic but efficient delay-constrained protocols, which can approach the performance upper bound reported in Part I, even in cases where only a small delay is permitted. The proposed protocols not only consider the instantaneous qualities of the involved links for adaptive mode selection but also take the states of the queues at the buffers into account, i.e., the number of packets in the queues. The average throughput and the average delay of the proposed delay-constrained protocols are evaluated by analyzing the Markov chain of the states of the queues. Numerical results show that the proposed protocols outperform existing bidirectional relaying protocols for delay-constrained transmission.

In Scalable Video Coding (SVC), motion estimation and inter-layer prediction play an important role in elimination of temporal and spatial redundancies between consecutive layers. This paper evaluates the performance of widely accepted block matching algorithms used in various video compression standards, with emphasis on the performance of the algorithms for a didactic scalable video codec. Many different implementations of Fast Motion Estimation Algorithms have been proposed to reduce motion estimation complexity. The block matching algorithms have been analyzed with emphasis on Peak Signal to Noise Ratio (PSNR) and computations using MATLAB. In addition to the above comparisons, a survey has been done on Spiral Search Motion Estimation Algorithms for Video Coding. A New Modified Spiral Search (NMSS) motion estimation algorithm has been proposed with lower computational complexity. The proposed algorithm achieves 72% reduction in computation with a minimal (<1dB) reduction in PSNR. A brief introduction to the entire flow of video compression H.264/SVC is also presented in this paper.

H.264 Scalable Video Coding (SVC) is an extension to the Advanced Video Coding (AVC) H.264 standard which provides efficient scalability functionalities on top of the high coding efficiency of H.264/AVC. SVC allows for temporal, spatial, and quality scalability of the output video stream, encoding the video information into an H.264/AVC base layer and a series of enhancement layers which incrementally improve the quality, increase screen resolution and/or frame rate. SVC is particularly suited for mobile TV reception, since the received video quality is adaptable to variable reception conditions and heterogeneous receiver capabilities. However, mobile TV Digital Video Broadcasting (DVB) standards such as DVB-H and DVB-SH were designed prior to the introduction of SVC, and therefore the underlying transmission protocols are not optimized for scalable video delivery. In this overview paper, we review recently proposed solutions for SVC stream adaptation on the underlying DVB-H/SH protocols, and point out novel technical solutions that are currently under consideration for the next generation mobile broadcasting standard DVB-NGH.

Bit-depth scalability compatible to H.264/AVC-scalable extension

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.