Wyner-Ziv to H.264 Video Transcoder for Low Cost Video Encoding

This paper proposes a Wyner-Ziv / H.264 transcoder that enables low cost video applications. The proposed solution supports video encoding on resource constrained devices such as disposable video cameras, network camcorders and low cost video encoders. This approach is based on reducing encoding resource requirements on a device by using Wyner-Ziv video encoding. The system shifts the burden of complexity away from the encoder, for example to a network node, where a transcoder efficiently converts WZ encoded video to H.264 by reusing the information from the WZ decoding stage. The transcoded H.264 video is require fewer resources than WZ decoding and therefore reduces the complexity of decoding. The complexity of encoding and playback ends of video applications is thus reduced enabling new class of. consumer application. The paper is focused on reducing the complexity of the macro-block mode coding decision process carried out in H.264 encoding stage of the transcoder. Based on a data mining process, the approach replaces the high complexity H.264 mode decision algorithm by a faster decision tree. The proposed architecture reduces the battery consumption of the end-user devices and the transcoding time is reduced by 86% with negligible rate-distortion loss

Wyner-Ziv to H.264 Video Transcoder for Low Cost Video Encoding

This paper proposes a Wyner-Ziv / H.264 transcoder that enables low cost video applications. The proposed solution supports video encoding on resource constrained devices such as disposable video cameras, network camcorders and low cost video encoders. This approach is based on reducing encoding resource requirements on a device by using Wyner-Ziv video encoding. The system shifts the burden of complexity away from the encoder, for example to a network node, where a transcoder efficiently converts WZ encoded video to H.264 by reusing the information from the WZ decoding stage. The transcoded H.264 video is require fewer resources than WZ decoding and therefore reduces the complexity of decoding. The complexity of encoding and playback ends of video applications is thus reduced enabling new class of. consumer application. The paper is focused on reducing the complexity of the macro-block mode coding decision process carried out in H.264 encoding stage of the transcoder. Based on a data mining process, the approach replaces the high complexity H.264 mode decision algorithm by a faster decision tree. The proposed architecture reduces the battery consumption of the end-user devices and the transcoding time is reduced by 86% with negligible rate-distortion loss

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Advanced heterogeneous video transcoding

PhDVideo transcoding is an essential tool to promote inter-operability between different video communication systems. This thesis presents two novel video transcoders, both operating on bitstreams of the cur- rent H.264/AVC standard. The first transcoder converts H.264/AVC bitstreams to a Wavelet Scalable Video Codec (W-SVC), while the second targets the emerging High Efficiency Video Coding (HEVC). Scalable Video Coding (SVC) enables low complexity adaptation of compressed video, providing an efficient solution for content delivery through heterogeneous networks. The transcoder proposed here aims at exploiting the advantages offered by SVC technology when dealing with conventional coders and legacy video, efficiently reusing information found in the H.264/AVC bitstream to achieve a high rate-distortion performance at a low complexity cost. Its main features include new mode mapping algorithms that exploit the W-SVC larger macroblock sizes, and a new state-of-the-art motion vector composition algorithm that is able to tackle different coding configurations in the H.264/AVC bitstream, including IPP or IBBP with multiple reference frames. The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. This thesis proposes and evaluates several transcoding algorithms for the HEVC codec. In particular, a transcoder based on a new method that is capable of complexity scalability, trading off rate-distortion performance for complexity reduction, is proposed. Furthermore, other transcoding solutions are explored, based on a novel content-based modeling approach, in which the transcoder adapts its parameters based on the contents of the sequence being encoded. Finally, the application of this research is not constrained to these transcoders, as many of the techniques developed aim to contribute to advance the research on this field, and have the potential to be incorporated in different video transcoding architectures