Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Complexity Analysis Of Next-Generation VVC Encoding and Decoding

While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.Comment: IEEE ICIP 202

Information fusion based techniques for HEVC

Aiming at the conflict circumstances of multi-parameter H.265/HEVC encoder system, the present paper introduces the analysis of many optimizations\u27 set in order to improve the trade-off between quality, performance and power consumption for different reliable and accurate applications. This method is based on the Pareto optimization and has been tested with different resolutions on real-time encoders

A comprehensive video codec comparison

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency

An Analysis of VP8, a new video codec for the web

Video is an increasingly ubiquitous part of our lives. Fast and efficient video codecs are necessary to satisfy the increasing demand for video on the web and mobile devices. However, open standards and patent grants are paramount to the adoption of video codecs across different platforms and browsers. Google On2 released VP8 in May 2010 to compete with H.264, the current standard of video codecs, complete with source code, specification and a perpetual patent grant. As the amount of video being created every day is growing rapidly, the decision of which codec to encode this video with is paramount; if a low quality codec or a restrictively licensed codec is used, the video recorded might be of little to no use. We sought to study VP8 and its quality versus its resource consumption compared to H.264 -- the most popular current video codec -- so that reader may make an informed decision for themselves or for their organizations about whether to use H.264 or VP8, or something else entirely. We examined VP8 in detail, compared its theoretical complexity to H.264 and measured the efficiency of its current implementation. VP8 shares many facets of its design with H.264 and other Discrete Cosine Transform (DCT) based video codecs. However, VP8 is both simpler and less feature rich than H.264, which may allow for rapid hardware and software implementations. As it was designed for the Internet and newer mobile devices, it contains fewer legacy features, such as interlacing, than H.264 supports. To perform quality measurements, the open source VP8 implementation libvpx was used. This is the reference implementation. For H.264, the open source H.264 encoder x264 was used. This encoder has very high performance, and is often rated at the top of its field in efficiency. The JM reference encoder was used to establish a baseline quality for H.264. Our findings indicate that VP8 performs very well at low bitrates, at resolutions at and below CIF. VP8 may be able to successfully displace H.264 Baseline in the mobile streaming video domain. It offers higher quality at a lower bitrate for low resolution images due to its high performing entropy coder and non-contiguous macroblock segmentation. At higher resolutions, VP8 still outperforms H.264 Baseline, but H.264 High profile leads. At HD resolution (720p and above), H.264 is significantly better than VP8 due to its superior motion estimation and adaptive coding. There is little significant difference between the intra-coding performance between H.264 and VP8. VP8\u27s in-loop deblocking filter outperforms H.264\u27s version. H.264\u27s inter-coding, with full support for B frames and weighting outperforms VP8\u27s alternate reference scheme, although this may improve in the future. On average, VP8\u27s feature set is less complex than H.264\u27s equivalents, which, along with its open source implementation, may spur development in the future. These findings indicate that VP8 has strong fundamentals when compared with H.264, but that it lacks optimization and maturity. It will likely improve as engineers optimize VP8\u27s reference implementation, or when a competing implementation is developed. We recommend several areas that the VP8 developers should focus on in the future

Algorithms and Hardware Co-Design of HEVC Intra Encoders

Digital video is becoming extremely important nowadays and its importance has greatly increased in the last two decades. Due to the rapid development of information and communication technologies, the demand for Ultra-High Definition (UHD) video applications is becoming stronger. However, the most prevalent video compression standard H.264/AVC released in 2003 is inefficient when it comes to UHD videos. The increasing desire for superior compression efficiency to H.264/AVC leads to the standardization of High Efficiency Video Coding (HEVC). Compared with the H.264/AVC standard, HEVC offers a double compression ratio at the same level of video quality or substantial improvement of video quality at the same video bitrate. Yet, HE-VC/H.265 possesses superior compression efficiency, its complexity is several times more than H.264/AVC, impeding its high throughput implementation. Currently, most of the researchers have focused merely on algorithm level adaptations of HEVC/H.265 standard to reduce computational intensity without considering the hardware feasibility. What’s more, the exploration of efficient hardware architecture design is not exhaustive. Only a few research works have been conducted to explore efficient hardware architectures of HEVC/H.265 standard. In this dissertation, we investigate efficient algorithm adaptations and hardware architecture design of HEVC intra encoders. We also explore the deep learning approach in mode prediction. From the algorithm point of view, we propose three efficient hardware-oriented algorithm adaptations, including mode reduction, fast coding unit (CU) cost estimation, and group-based CABAC (context-adaptive binary arithmetic coding) rate estimation. Mode reduction aims to reduce mode candidates of each prediction unit (PU) in the rate-distortion optimization (RDO) process, which is both computation-intensive and time-consuming. Fast CU cost estimation is applied to reduce the complexity in rate-distortion (RD) calculation of each CU. Group-based CABAC rate estimation is proposed to parallelize syntax elements processing to greatly improve rate estimation throughput. From the hardware design perspective, a fully parallel hardware architecture of HEVC intra encoder is developed to sustain UHD video compression at 4K@30fps. The fully parallel architecture introduces four prediction engines (PE) and each PE performs the full cycle of mode prediction, transform, quantization, inverse quantization, inverse transform, reconstruction, rate-distortion estimation independently. PU blocks with different PU sizes will be processed by the different prediction engines (PE) simultaneously. Also, an efficient hardware implementation of a group-based CABAC rate estimator is incorporated into the proposed HEVC intra encoder for accurate and high-throughput rate estimation. To take advantage of the deep learning approach, we also propose a fully connected layer based neural network (FCLNN) mode preselection scheme to reduce the number of RDO modes of luma prediction blocks. All angular prediction modes are classified into 7 prediction groups. Each group contains 3-5 prediction modes that exhibit a similar prediction angle. A rough angle detection algorithm is designed to determine the prediction direction of the current block, then a small scale FCLNN is exploited to refine the mode prediction