In real-time, two way video communications, how to minimize the end-to-end delay for transmitting video data is very important. Since the delay produced by bits accumulated in the encoder buffer must be very small, we need an improved rate control to encode the video with high quality and maintain low buffer fullness. One approach to reduce the buffer fullness is to skip the encoding frames, but the frame-skipping will produce undesirable motion discontinuity in the encoded video sequence. In this thesis, we study the impact of low delay constraint in H.264 rate control and its improvements. The drawback of the H.264 rate control is it cannot handle the frame-skipping mechanism well. To modify this, we control the quantization parameter of each I-frame to avoid the buffer overflow and frame-skipping. Since encoding the I-frame by different quantization parameter will generate different rate and distortion for a group of pictures (GOP), we use Lagrangian optimization to find the tradeoff between rate and distortion for a GOP. By the estimation models of rate and distortion for a GOP, calculate the Lagrangian cost for each possible quantization parameter of I-frame, the quantization parameter with minimum Lagrangian cost will be our choice for I-frame. Simulation results show that our proposed rate control encode the video sequence with less skipped frames and with higher PSNR compared to H.264 rate control under low delay constraint.

<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>

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.