Improved Method to Select the Lagrange Multiplier for Rate-Distortion Based Motion Estimation in Video Coding

The motion estimation (ME) process used in the H.264/AVC reference software is based on minimizing a cost function that involves two terms (distortion and rate) that are properly balanced through a Lagrangian parameter, usually denoted as lambda(motion). In this paper we propose an algorithm to improve the conventional way of estimating lambda(motion) and, consequently, the ME process. First, we show that the conventional estimation of lambda(motion) turns out to be significantly less accurate when ME-compromising events, which make the ME process to perform poorly, happen. Second, with the aim of improving the coding efficiency in these cases, an efficient algorithm is proposed that allows the encoder to choose between three different values of lambda(motion) for the Inter 16x16 partition size. To be more precise, for this partition size, the proposed algorithm allows the encoder to additionally test lambda(motion) = 0 and lambda(motion) arbitrarily large, which corresponds to minimum distortion and minimum rate solutions, respectively. By testing these two extreme values, the algorithm avoids making large ME errors. The experimental results on video segments exhibiting this type of ME-compromising events reveal an average rate reduction of 2.20% for the same coding quality with respect to the JM15.1 reference software of H.264/AVC. The algorithm has been also tested in comparison with a state-of-the-art algorithm called context adaptive Lagrange multiplier. Additionally, two illustrative examples of the subjective performance improvement are provided.This work has been partially supported by the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad

An Energy-efficient Live Video Coding and Communication over Unreliable Channels

In the field of multimedia communications there exist many important applications where live or real-time video data is captured by a camera, compressed and transmitted over the channel which can be very unreliable and, at the same time, computational resources or battery capacity of the transmission device are very limited. For example, such scenario holds for video transmission for space missions, vehicle-to-infrastructure video delivery, multimedia wireless sensor networks, wireless endoscopy, video coding on mobile phones, high definition wireless video surveillance and so on. Taking into account such restrictions, a development of efficient video coding techniques for these applications is a challenging problem. The most popular video compression standards, such as H.264/AVC, are based on the hybrid video coding concept, which is very efficient when video encoding is performed off-line or non real-time and the pre-encoded video is played back. However, the high computational complexity of the encoding and the high sensitivity of the hybrid video bit stream to losses in the communication channel constitute a significant barrier of using these standards for the applications mentioned above. In this thesis, as an alternative to the standards, a video coding based on three-dimensional discrete wavelet transform (3-D DWT) is considered as a candidate to provide a good trade-off between encoding efficiency, computational complexity and robustness to channel losses. Efficient tools are proposed to reduce the computational complexity of the 3-D DWT codec. These tools cover all levels of the codec’s development such as adaptive binary arithmetic coding, bit-plane entropy coding, wavelet transform, packet loss protection based on error-correction codes and bit rate control. These tools can be implemented as end-to-end solution and directly used in real-life scenarios. The thesis provides theoretical, simulation and real-world results which show that the proposed 3-D DWT codec can be more preferable than the standards for live video coding and communication over highly unreliable channels and or in systems where the video encoding computational complexity or power consumption plays a critical role

Surveillance centric coding

PhDThe research work presented in this thesis focuses on the development of techniques specific to surveillance videos for efficient video compression with higher processing speed. The Scalable Video Coding (SVC) techniques are explored to achieve higher compression efficiency. The framework of SVC is modified to support Surveillance Centric Coding (SCC). Motion estimation techniques specific to surveillance videos are proposed in order to speed up the compression process of the SCC. The main contributions of the research work presented in this thesis are divided into two groups (i) Efficient Compression and (ii) Efficient Motion Estimation. The paradigm of Surveillance Centric Coding (SCC) is introduced, in which coding aims to achieve bit-rate optimisation and adaptation of surveillance videos for storing and transmission purposes. In the proposed approach the SCC encoder communicates with the Video Content Analysis (VCA) module that detects events of interest in video captured by the CCTV. Bit-rate optimisation and adaptation are achieved by exploiting the scalability properties of the employed codec. Time segments containing events relevant to surveillance application are encoded using high spatiotemporal resolution and quality while the irrelevant portions from the surveillance standpoint are encoded at low spatio-temporal resolution and / or quality. Thanks to the scalability of the resulting compressed bit-stream, additional bit-rate adaptation is possible; for instance for the transmission purposes. Experimental evaluation showed that significant reduction in bit-rate can be achieved by the proposed approach without loss of information relevant to surveillance applications. In addition to more optimal compression strategy, novel approaches to performing efficient motion estimation specific to surveillance videos are proposed and implemented with experimental results. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Different approaches for selective motion estimation, GOP based, Frame based and Block based, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when a moving object is detected for any frame of the GOP. iii While for the Frame based approach; each frame is tested for the motion activity and consequently for selective motion estimation. The selective motion estimation approach is further explored at a lower level as Block based selective motion estimation. Experimental evaluation showed that significant reduction in computational complexity can be achieved by applying the proposed strategy. In addition to selective motion estimation, a tracker based motion estimation and fast full search using multiple reference frames has been proposed for the surveillance videos. Extensive testing on different surveillance videos shows benefits of application of proposed approaches to achieve the goals of the SCC

Energy-efficient bandwidth allocation for multiuser scalable video streaming over WLAN

We consider the problem of packet scheduling for the transmission of multiple video streams over a wireless local area network (WLAN). A cross-layer optimization framework is proposed to minimize the wireless transceiver energy consumption while meeting the user required visual quality constraints. The framework relies on the IEEE 802.11 standard and on the embedded bitstream structure of the scalable video coding scheme. It integrates an application-level video quality metric as QoS constraint (instead of a communication layer quality metric) with energy consumption optimization through link layer scaling and sleeping. Both energy minimization and min-max energy optimization strategies are discussed. Simulation results demonstrate significant energy gains compared to the state-of-the-art approaches

Efficient compression of motion compensated residuals

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Rate distortion control in digital video coding

Lossy compression is widely applied for coding visual information in applications such as entertainment in order to achieve a high compression ratio. In this case, the video quality worsens as the compression ratio increases. Rate control tries to use the bit budget properly so the visual distortion is minimized. Rate control for H.264, the state-of-the-art hybrid video coder, is investigated. Based on the Rate-Distortion (R-D) slope analysis, an operational rate distortion optimization scheme for H.264 using Lagrangian multiplier method is proposed. The scheme tries to find the best path of quantization parameter (OP) options at each macroblock. The proposed scheme provides a smoother rate control that is able to cover a wider range of bit rates and for many sequences it outperforms the H.264 (JM92 version) rate control scheme in the sense of PSNR. The Bath University Matching Pursuit (BUMP) project develops a new matching pursuit (MP) technique as an alternative to transform video coders. By combining MP with precision limited quantization (PLO) and multi-pass embedded residual group encoder (MERGE), a very efficient coder is built that is able to produce an embedded bit stream, which is highly desirable for rate control. The problem of optimal bit allocation with a BUMP based video coder is investigated. An ad hoc scheme of simply limiting the maximum atom number shows an obvious performance improvement, which indicates a potential of efficiency improvement. An in depth study on the bit Rate-Atom character has been carried out and a rate estimation model has been proposed. The model gives a theoretical description of how the oit number changes. An adaptive rate estimation algorithm has been proposed. Experiments show that the algorithm provides extremely high estimation accuracy. The proposed R-D source model is then applied to bit allocation in the BUMP based video coder. An R-D slope unifying scheme was applied to optimize the performance of the coder'. It adopts the R-D model and fits well within the BUMP coder. The optimization can be performed in a straightforward way. Experiments show that the proposed method greatly improved performance of BUMP video coder, and outperforms H.264 in low and medium bit rates by up to 2 dB.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Variable Block Size Motion Compensation In The Redundant Wavelet Domain

Video is one of the most powerful forms of multimedia because of the extensive information it delivers. Video sequences are highly correlated both temporally and spatially, a fact which makes the compression of video possible. Modern video systems employ motion estimation and motion compensation (ME/MC) to de-correlate a video sequence temporally. ME/MC forms a prediction of the current frame using the frames which have been already encoded. Consequently, one needs to transmit the corresponding residual image instead of the original frame, as well as a set of motion vectors which describe the scene motion as observed at the encoder. The redundant wavelet transform (RDWT) provides several advantages over the conventional wavelet transform (DWT). The RDWT overcomes the shift invariant problem in DWT. Moreover, RDWT retains all the phase information of wavelet coefficients and provides multiple prediction possibilities for ME/MC in wavelet domain. The general idea of variable size block motion compensation (VSBMC) technique is to partition a frame in such a way that regions with uniform translational motions are divided into larger blocks while those containing complicated motions into smaller blocks, leading to an adaptive distribution of motion vectors (MV) across the frame. The research proposed new adaptive partitioning schemes and decision criteria in RDWT that utilize more effectively the motion content of a frame in terms of various block sizes. The research also proposed a selective subpixel accuracy algorithm for the motion vector using a multiband approach. The selective subpixel accuracy reduces the computations produced by the conventional subpixel algorithm while maintaining the same accuracy. In addition, the method of overlapped block motion compensation (OBMC) is used to reduce blocking artifacts. Finally, the research extends the applications of the proposed VSBMC to the 3D video sequences. The experimental results obtained here have shown that VSBMC in the RDWT domain can be a powerful tool for video compression

SSIM-Inspired Quality Assessment, Compression, and Processing for Visual Communications

Objective Image and Video Quality Assessment (I/VQA) measures predict image/video quality as perceived by human beings - the ultimate consumers of visual data. Existing research in the area is mainly limited to benchmarking and monitoring of visual data. The use of I/VQA measures in the design and optimization of image/video processing algorithms and systems is more desirable, challenging and fruitful but has not been well explored. Among the recently proposed objective I/VQA approaches, the structural similarity (SSIM) index and its variants have emerged as promising measures that show superior performance as compared to the widely used mean squared error (MSE) and are computationally simple compared with other state-of-the-art perceptual quality measures. In addition, SSIM has a number of desirable mathematical properties for optimization tasks. The goal of this research is to break the tradition of using MSE as the optimization criterion for image and video processing algorithms. We tackle several important problems in visual communication applications by exploiting SSIM-inspired design and optimization to achieve significantly better performance. Firstly, the original SSIM is a Full-Reference IQA (FR-IQA) measure that requires access to the original reference image, making it impractical in many visual communication applications. We propose a general purpose Reduced-Reference IQA (RR-IQA) method that can estimate SSIM with high accuracy with the help of a small number of RR features extracted from the original image. Furthermore, we introduce and demonstrate the novel idea of partially repairing an image using RR features. Secondly, image processing algorithms such as image de-noising and image super-resolution are required at various stages of visual communication systems, starting from image acquisition to image display at the receiver. We incorporate SSIM into the framework of sparse signal representation and non-local means methods and demonstrate improved performance in image de-noising and super-resolution. Thirdly, we incorporate SSIM into the framework of perceptual video compression. We propose an SSIM-based rate-distortion optimization scheme and an SSIM-inspired divisive optimization method that transforms the DCT domain frame residuals to a perceptually uniform space. Both approaches demonstrate the potential to largely improve the rate-distortion performance of state-of-the-art video codecs. Finally, in real-world visual communications, it is a common experience that end-users receive video with significantly time-varying quality due to the variations in video content/complexity, codec configuration, and network conditions. How human visual quality of experience (QoE) changes with such time-varying video quality is not yet well-understood. We propose a quality adaptation model that is asymmetrically tuned to increasing and decreasing quality. The model improves upon the direct SSIM approach in predicting subjective perceptual experience of time-varying video quality