No-reference bitstream-based impairment detection for high efficiency video coding

Video distribution over error-prone Internet Protocol (IP) networks results in visual impairments on the received video streams. Objective impairment detection algorithms are crucial for maintaining a high Quality of Experience (QoE) as provided with IPTV distribution. There is a lot of research invested in H.264/AVC impairment detection models and questions rise if these turn obsolete with a transition to the successor of H.264/AVC, called High Efficiency Video Coding (HEVC). In this paper, first we show that impairments on HEVC compressed sequences are more visible compaired to H.264/AVC encoded sequences. We also show that an impairment detection model designed for H.264/AVC could be reused on HEVC, but that caution is advised. A more accurate model taking into account content classification needed slight modification to remain applicable for HEVC compression video content

Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

Low complexity video compression using moving edge detection based on DCT coefficients

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges us- ing only DCT coe±cients. The detection, whilst being very e±cient, also allows e±cient motion estimation by constraining the search process to moving macro-blocks only. The encoders PSNR is degraded by 2dB com- pared to H.264/AVC inter for such scenarios, whilst requiring only 5% of the execution time. The computational complexity of our approach is comparable to that of the DISCOVER codec which is the state of the art low complexity distributed video coding. The proposed method ¯nds blocks with moving edge blocks and processes only selected blocks. The approach is particularly suited to surveillance type scenarios with a static camera

Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)

Variable Block Size (VBS) based motion estimation has been adapted in state of the art video coding, such as H.264/AVC, VC-1. However, a low complexity H.264/AVC encoder cannot take advantage of VBS due to its power consumption requirements. In this paper, we present a VBS partition algorithm based on a binary motion edge map without either initial motion estimation or Rate-Distortion (R-D) optimization for selecting modes. The proposed algorithm uses the Walsh Hadamard Transform (WHT) to create a binary edge map, which provides a computational complexity cost effectiveness compared to other light segmentation methods typically used to detect the required region

A Convolutional Neural Network Approach for Half-Pel Interpolation in Video Coding

Motion compensation is a fundamental technology in video coding to remove the temporal redundancy between video frames. To further improve the coding efficiency, sub-pel motion compensation has been utilized, which requires interpolation of fractional samples. The video coding standards usually adopt fixed interpolation filters that are derived from the signal processing theory. However, as video signal is not stationary, the fixed interpolation filters may turn out less efficient. Inspired by the great success of convolutional neural network (CNN) in computer vision, we propose to design a CNN-based interpolation filter (CNNIF) for video coding. Different from previous studies, one difficulty for training CNNIF is the lack of ground-truth since the fractional samples are actually not available. Our solution for this problem is to derive the "ground-truth" of fractional samples by smoothing high-resolution images, which is verified to be effective by the conducted experiments. Compared to the fixed half-pel interpolation filter for luma in High Efficiency Video Coding (HEVC), our proposed CNNIF achieves up to 3.2% and on average 0.9% BD-rate reduction under low-delay P configuration.Comment: International Symposium on Circuits and Systems (ISCAS) 201