Subjective evaluation of HEVC intra coding for still image compression

High Efficiency Video Coding (HEVC) demonstrates a significant improvement in compression efficiency compared to H.264/MPEG-4 AVC, especially for video with resolution beyond HD, such as 4K UHDTV. One advantage of HEVC is the improved intra coding of video frames. Hence, it is natural to question how such intra coding compares to state of the art compression codecs for still images. This paper attempts to answer this question by providing a detailed analysis and performance comparison of HEVC intra coding with JPEG and JPEG 2000 (both 4:2:0 and 4:4:4 configurations) via a series of subjective and objective evaluations. The evaluation results demonstrate that HEVC intra coding outperforms standard codecs for still images with the average bit rate reduction ranging from 16% (compared to JPEG 2000 4:4:4) up to 43% (compared to JPEG). These findings imply that both still images and moving pictures can be efficiently compressed by the same coding algorithm with higher compression efficiency

Multi-loop quality scalability based on high efficiency video coding

Scalable video coding performance largely depends on the underlying single layer coding efficiency. In this paper, the quality scalability capabilities are evaluated on a base of the new High Efficiency Video Coding (HEVC) standard under development. To enable the evaluation, a multi-loop codec has been designed using HEVC. Adaptive inter-layer prediction is realized by including the lower layer in the reference list of the enhancement layer. As a result, adaptive scalability on frame level and on prediction unit level is accomplished. Compared to single layer coding, 19.4% Bjontegaard Delta bitrate increase is measured over approximately a 30dB to 40dB PSNR range. When compared to simulcast, 20.6% bitrate reduction can be achieved. Under equivalent conditions, the presented technique achieves 43.8% bitrate reduction over Coarse Grain Scalability of the SVC - H.264/AVC-based standard

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

Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models