Side information exploitation, quality control and low complexity implementation for distributed video coding

Distributed video coding (DVC) is a new video coding methodology that shifts the highly complex motion search components from the encoder to the decoder, such a video coder would have a great advantage in encoding speed and it is still able to achieve similar rate-distortion performance as the conventional coding solutions. Applications include wireless video sensor networks, mobile video cameras and wireless video surveillance, etc. Although many progresses have been made in DVC over the past ten years, there is still a gap in RD performance between conventional video coding solutions and DVC. The latest development of DVC is still far from standardization and practical use. The key problems remain in the areas such as accurate and efficient side information generation and refinement, quality control between Wyner-Ziv frames and key frames, correlation noise modelling and decoder complexity, etc. Under this context, this thesis proposes solutions to improve the state-of-the-art side information refinement schemes, enable consistent quality control over decoded frames during coding process and implement highly efficient DVC codec. This thesis investigates the impact of reference frames on side information generation and reveals that reference frames have the potential to be better side information than the extensively used interpolated frames. Based on this investigation, we also propose a motion range prediction (MRP) method to exploit reference frames and precisely guide the statistical motion learning process. Extensive simulation results show that choosing reference frames as SI performs competitively, and sometimes even better than interpolated frames. Furthermore, the proposed MRP method is shown to significantly reduce the decoding complexity without degrading any RD performance. To minimize the block artifacts and achieve consistent improvement in both subjective and objective quality of side information, we propose a novel side information synthesis framework working on pixel granularity. We synthesize the SI at pixel level to minimize the block artifacts and adaptively change the correlation noise model according to the new SI. Furthermore, we have fully implemented a state-of-the-art DVC decoder with the proposed framework using serial and parallel processing technologies to identify bottlenecks and areas to further reduce the decoding complexity, which is another major challenge for future practical DVC system deployments. The performance is evaluated based on the latest transform domain DVC codec and compared with different standard codecs. Extensive experimental results show substantial and consistent rate-distortion gains over standard video codecs and significant speedup over serial implementation. In order to bring the state-of-the-art DVC one step closer to practical use, we address the problem of distortion variation introduced by typical rate control algorithms, especially in a variable bit rate environment. Simulation results show that the proposed quality control algorithm is capable to meet user defined target distortion and maintain a rather small variation for sequence with slow motion and performs similar to fixed quantization for fast motion sequence at the cost of some RD performance. Finally, we propose the first implementation of a distributed video encoder on a Texas Instruments TMS320DM6437 digital signal processor. The WZ encoder is efficiently implemented, using rate adaptive low-density-parity-check accumulative (LDPCA) codes, exploiting the hardware features and optimization techniques to improve the overall performance. Implementation results show that the WZ encoder is able to encode at 134M instruction cycles per QCIF frame on a TMS320DM6437 DSP running at 700MHz. This results in encoder speed 29 times faster than non-optimized encoder implementation. We also implemented a highly efficient DVC decoder using both serial and parallel technology based on a PC-HPC (high performance cluster) architecture, where the encoder is running in a general purpose PC and the decoder is running in a multicore HPC. The experimental results show that the parallelized decoder can achieve about 10 times speedup under various bit-rates and GOP sizes compared to the serial implementation and significant RD gains with regards to the state-of-the-art DISCOVER codec

Enhanced low bitrate H.264 video coding using decoder-side super-resolution and frame interpolation

Advanced inter-prediction modes are introduced recently in literature to improve video coding performances of both H.264 and High Efficiency Video Coding standards. Decoder-side motion analysis and motion vector derivation are proposed to reduce coding costs of motion information. Here, we introduce enhanced skip and direct modes for H.264 coding using decoder-side super-resolution (SR) and frame interpolation. P-and B-frames are downsampled and H.264 encoded at lower resolution (LR). Then reconstructed LR frames are super-resolved using decoder-side motion estimation. Alternatively for B-frames, bidirectional true motion estimation is performed to synthesize a B-frame from its reference frames. For P-frames, bicubic interpolation of the LR frame is used as an alternative to SR reconstruction. A rate-distortion optimal mode selection algorithm is developed to decide for each MB which of the two reconstructions to use as skip/direct mode prediction. Simulations indicate an average of 1.04 dB peak signal-to-noise ratio (PSNR) improvement or 23.0% bitrate reduction at low bitrates when compared with H.264 standard. The PSNR gains reach as high as 3.00 dB for inter-predicted frames and 3.78 dB when only B-frames are considered. Decoded videos exhibit significantly better visual quality as well.This research was supported by TUBITAK Career Grant 108E201Publisher's Versio

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods