<p>To provide universal multimedia experience, multimedia streaming services need to transparently handle the variation and heterogeneity in operating environment. From the standpoint of streaming application, video adaptation techniques are intended to cope with the environmental variations by utilizing manipulations of the video content itself. Scalable video coding (SVC) schemes, like that suggested by the standards H.264 and its SVC extension, is highly attractive for designing a self-adaptive video streaming system. When SVC is employed in streaming system, the produced video stream can be then easily truncated or tailored to form several sub-streams which can be decoded separately to obtain a range of preferable picture size, quality and frame rate. However, questions about how to perform the adaptation using SVC and how much adaptation SVC enables are still remaining research issues. We still lack a thorough understanding of how to automate the scaling procedure in order to achieve an optimal video Quality-of-Experience for end users.</p><p>Video QoE, depends highly on human perception. In this thesis, we introduce several video QoE studies around the usability of H.264 SVC. Several factors that contribute significantly to the overall QoEs have been identified and evaluated in these studies. As an example of application usage related factor, playback smoothness and application response time are critical performance measures which can benefit from temporal scalability. Targeting on applications that requires frequent interactivity, we propose a transcoding scheme that fully utilizes the benefits of Switching P and Switching I frames specified in H.264 to enhance video stream's temporal scalability.  Focusing on visual quality related factors, a series of carefully designed subjective quality assessment tests have been performed on mobile devices to investigate the effects of multi-dimensional scalability on human quality perception. Our study reveals that QoE degrades non-monotonically with bitrate and that scaling order preferences are content-dependent. Another study find out that the flickering effect caused by frequent switching between layers in SVC compliant bit-streams is highly related to the switching period. When the period is above a certain threshold, the flickering effect will disappear and layer switching should not be considered as harmful. We have also examined user perceived video quality in 3D virtual worlds. Our results show that the avatars' distance to the virtual screen in 3D worlds contribute significant to the video QoE, i.e., for a wide extent of distortion, there exists always a feasible virtual distance from where the distortion is not detectable for most of people, which makes sense to perform video adaptation.</p><p>The work presented in this thesis is supposed to help improving the design of self adaptive video streaming services that can deliver video content independently of network technology and end-device capability while seeking the best possible experience for video.</p> / Ardendo småföretagsdoktorand

To provide universal multimedia experience, multimedia streaming services need to transparently handle the variation and heterogeneity in operating environment. From the standpoint of streaming application, video adaptation techniques are intended to cope with the environmental variations by utilizing manipulations of the video content itself. Scalable video coding (SVC) schemes, like that suggested by the standards H.264 and its SVC extension, is highly attractive for designing a self-adaptive video streaming system. When SVC is employed in streaming system, the produced video stream can be then easily truncated or tailored to form several sub-streams which can be decoded separately to obtain a range of preferable picture size, quality and frame rate. However, questions about how to perform the adaptation using SVC and how much adaptation SVC enables are still remaining research issues. We still lack a thorough understanding of how to automate the scaling procedure in order to achieve an optimal video Quality-of-Experience for end users. Video QoE, depends highly on human perception. In this thesis, we introduce several video QoE studies around the usability of H.264 SVC. Several factors that contribute significantly to the overall QoEs have been identified and evaluated in these studies. As an example of application usage related factor, playback smoothness and application response time are critical performance measures which can benefit from temporal scalability. Targeting on applications that requires frequent interactivity, we propose a transcoding scheme that fully utilizes the benefits of Switching P and Switching I frames specified in H.264 to enhance video stream's temporal scalability.  Focusing on visual quality related factors, a series of carefully designed subjective quality assessment tests have been performed on mobile devices to investigate the effects of multi-dimensional scalability on human quality perception. Our study reveals that QoE degrades non-monotonically with bitrate and that scaling order preferences are content-dependent. Another study find out that the flickering effect caused by frequent switching between layers in SVC compliant bit-streams is highly related to the switching period. When the period is above a certain threshold, the flickering effect will disappear and layer switching should not be considered as harmful. We have also examined user perceived video quality in 3D virtual worlds. Our results show that the avatars' distance to the virtual screen in 3D worlds contribute significant to the video QoE, i.e., for a wide extent of distortion, there exists always a feasible virtual distance from where the distortion is not detectable for most of people, which makes sense to perform video adaptation. The work presented in this thesis is supposed to help improving the design of self adaptive video streaming services that can deliver video content independently of network technology and end-device capability while seeking the best possible experience for video. / Ardendo småföretagsdoktorand

<p>H.264, also known as MPEG-4 Part 10: Advanced Video Coding, is the latest MPEG standard for video coding. It provides approximately 50% bit rate savings for equivalent perceptual quality compared to any previous standard. In the same fashion as previous MPEG standards, only the bitstream syntax and the decoder are specified. Hence, coding performance is not only determined by the standard itself but also by the implementation of the encoder. In this report we propose two methods for improving the coding performance while remaining fully compliant to the standard. </p><p>After transformation and quantization, the transform coefficients are usually entropy coded and embedded in the bitstream. However, some of them might be beneficial to discard if the number of saved bits are sufficiently large. This is usually referred to as coefficient thresholding and is investigated in the scope of H.264 in this report. </p><p>Lagrangian optimization for video compression has proven to yield substantial improvements in perceived quality and the H.264 Reference Software has been designed around this concept. When performing Lagrangian optimization, lambda is a crucial parameter that determines the tradeoff between rate and distortion. We propose a new method to select lambda and the quantization parameter for non-reference frames in H.264. </p><p>The two methods are shown to achieve significant improvements. When combined, they reduce the bitrate around 12%, while preserving the video quality in terms of average PSNR. </p><p>To aid development of H.264, a software tool has been created to visualize the coding process and present statistics. This tool is capable of displaying information such as bit distribution, motion vectors, predicted pictures and motion compensated block sizes.</p>

<p>ITU - T and the Moving Picture Expert Group (MPEG) have jointly, under the name of Joint Video Team (JVT), developed a new video coding standard. The standard is called H.264 and is also known as Advanced Video Coding (AVC) or MPEG-4 part 10. Comparisons shows that H.264 greatly outperforms MPEG-2, currently used in DVD and digital TV. H.264 halves the bit rate with equal image quality. The great rate - distortion performance means nevertheless a high computational complexity. Especially on the encoder side.</p><p>Handling of audio and video, e.g. compressing and filtering, is quite complex and requires high performance hardware and software. A video encoder consists of a number of modules that find the best coding parameters. For each macroblock several $modes$ are evaluated in order to achieve optimal coding. The reference implementation of H.264 uses a brute force search for this mode selection which is extremely computational constraining. In order to perform video encoding with satisfactory speed there is an obvious need for reducing the amount of modes that are evaluated.</p><p>This thesis proposes an algorithm which reduces the number of modes and reference frames that are evaluated. The algorithm can be regulated in order to fulfill the demand on quality versus speed. Six times faster encoding can be obtained without loosing perceptual image quality. By allowing some quality degradation the encoding becomes up to 20 times faster.</p>

H.264, also known as MPEG-4 Part 10: Advanced Video Coding, is the latest MPEG standard for video coding. It provides approximately 50% bit rate savings for equivalent perceptual quality compared to any previous standard. In the same fashion as previous MPEG standards, only the bitstream syntax and the decoder are specified. Hence, coding performance is not only determined by the standard itself but also by the implementation of the encoder. In this report we propose two methods for improving the coding performance while remaining fully compliant to the standard. After transformation and quantization, the transform coefficients are usually entropy coded and embedded in the bitstream. However, some of them might be beneficial to discard if the number of saved bits are sufficiently large. This is usually referred to as coefficient thresholding and is investigated in the scope of H.264 in this report. Lagrangian optimization for video compression has proven to yield substantial improvements in perceived quality and the H.264 Reference Software has been designed around this concept. When performing Lagrangian optimization, lambda is a crucial parameter that determines the tradeoff between rate and distortion. We propose a new method to select lambda and the quantization parameter for non-reference frames in H.264. The two methods are shown to achieve significant improvements. When combined, they reduce the bitrate around 12%, while preserving the video quality in terms of average PSNR. To aid development of H.264, a software tool has been created to visualize the coding process and present statistics. This tool is capable of displaying information such as bit distribution, motion vectors, predicted pictures and motion compensated block sizes.

ITU - T and the Moving Picture Expert Group (MPEG) have jointly, under the name of Joint Video Team (JVT), developed a new video coding standard. The standard is called H.264 and is also known as Advanced Video Coding (AVC) or MPEG-4 part 10. Comparisons shows that H.264 greatly outperforms MPEG-2, currently used in DVD and digital TV. H.264 halves the bit rate with equal image quality. The great rate - distortion performance means nevertheless a high computational complexity. Especially on the encoder side. Handling of audio and video, e.g. compressing and filtering, is quite complex and requires high performance hardware and software. A video encoder consists of a number of modules that find the best coding parameters. For each macroblock several $modes$ are evaluated in order to achieve optimal coding. The reference implementation of H.264 uses a brute force search for this mode selection which is extremely computational constraining. In order to perform video encoding with satisfactory speed there is an obvious need for reducing the amount of modes that are evaluated. This thesis proposes an algorithm which reduces the number of modes and reference frames that are evaluated. The algorithm can be regulated in order to fulfill the demand on quality versus speed. Six times faster encoding can be obtained without loosing perceptual image quality. By allowing some quality degradation the encoding becomes up to 20 times faster.