**Part 1**

**Compression** 

VIII Preface

image chain. The chapter also presents both objective image metrics and analysts'

Chapter four presents an overview of H.264 motion estimation and its types and also the various estimation criterions that decides the complexity of the chosen algorithm.

Chapter five is a systematic review of the pixel domain based global motion estimation approaches. The chapter discusses shortcomings in noise filtering and computational cost, the improvement approaches including hierarchical global motion estimation, partial pixel set based global motion estimation and compressed domain based global motion estimation are provided. Four global motion based applications including GMC/LMC in MPEG-4 video coding standard, global motion based sport video shot classification, GM/LM based error concealment and text occluded region recovery are

Chapter six argues that exploiting saliency-based video compression is a challenging and exciting area of research and especially nowadays when saliency models include more and more top-down information and manages to better and better predict real human gaze. Multimedia applications are a continuously evolving domain and compression algorithms must also evolve and adapt to new applications. The explosion of portable devices with less bandwidth and smaller screens, but also the future semantic TV/web and its object-based description will lead in the future to a higher importance of saliency-

The large amount of studies developed for this purpose related to quality assessment gives a general idea about the importance of this theme in video compression. The evolution of metrics and techniques is constant, finding the best ways of evaluating

Chapter seven describes a state of the art in quality assessment and techniques of subjective and objective assessment, with the most common artefacts and impairments

I wish to thank all the authors who have contributed to this book. I hope that by reading this book you will get many useful ideas for your own research, which will help to bridge the gap between video compression technology and applications.

I also hope this book is enjoyable to read and will further contribute to video compression, which requires a further interest and attention in both research and

The School of Engineering and Advanced Technology, Massey University (Turitea),

**Dr Amal Punchihewa** 

New Zealand

PhD, MEEng, BSC(Eng)Hons, CEng, FIET, MIPENZ,

MIEEE, MSLAAS , MCS, Leader - Multi-Media Research Group,

based algorithms for multimedia data repurposing and compression.

assessments of various compressed products.

described in this chapter.

the quality of video sequences.

application fields.

derived from compression and transmission.

**1** 

*Massey University* 

*Palmerston North New Zealand* 

**Compressive Video Coding:** 

*School of Engineering and Advanced Technology* 

**A Review of the State-Of-The-Art** 

Muhammad Yousuf Baig, Edmund M-K. Lai and Amal Punchihewa

Video coding and its related applications have advanced quite substantially in recent years. Major coding standards such as MPEG [1] and H.26x [2] are well developed and widely deployed. These standards are developed mainly for applications such as DVDs where the compressed video is played over many times by the consumer. Since compression only needs to be performed once while decompression (playback) is performed many times, it is desirable that the decoding/decompression process can be done as simply and quickly as possible. Therefore, essentially all current video compression schemes, such as the various MPEG standards as well as H.264 [1, 2] involve a complex encoder and a simple decoder. The exploitation of spatial and temporal redundancies for data compression at the encoder causes the encoding process to be typically 5 to 10 times more complex computationally than the decoder [3]. In order that video encoding can be performed in real time at frame rates of 30 frames per second or more, the encoding process has to be performed by

In the past ten years, we have seen substantial research and development of large sensor networks where a large number of sensors are deployed. For some applications such as video surveillance and sports broadcasting, these sensors are in fact video cameras. For such systems, there is a need to re-evaluate conventional strategies for video coding. If the encoders are made simpler, then the cost of a system involving tens or hundreds of cameras can be substantially reduced in comparison with deploying current camera systems. Typically, data from these cameras can be sent to a single decoder and aggregated. Since some of the scenes captured may be correlated, computational gain can potentially be achieved by decoding these scenes together rather than separately. . Decoding can be simple reconstruction of the video frames or it can be combined with detection algorithms specific to the application at hand. Thus there are benefits in combing reduced complexity cameras with flexible decoding processes to deliver modern applications which are not anticipated

Recently, a new theory called Compressed Sensing (CS) [4, 5, 6] has been developed which provides us with a completely new approach to data acquisition. In essence, CS tells us that

specially designed hardware, thus increasing the cost of cameras.

when the various video coding standards are developed.

**1. Introduction** 
