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Articoli di riviste sul tema "Scalable video compression"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 19 febbraio 2022

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1

Secker, A., e D. Taubman. "Highly scalable video compression with scalable motion coding". IEEE Transactions on Image Processing 13, n. 8 (agosto 2004): 1029–41. http://dx.doi.org/10.1109/tip.2004.826089.

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2

Singh, A., J. Bove e V. Mkhael. "Multidimensional quantizers for scalable video compression". IEEE Journal on Selected Areas in Communications 11, n. 1 (1993): 36–45. http://dx.doi.org/10.1109/49.210542.

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3

Ke Shen e E. J. Delp. "Wavelet based rate scalable video compression". IEEE Transactions on Circuits and Systems for Video Technology 9, n. 1 (1999): 109–22. http://dx.doi.org/10.1109/76.744279.

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4

Illgner, K., e F. Muller. "Spatially scalable video compression employing resolution pyramids". IEEE Journal on Selected Areas in Communications 15, n. 9 (1997): 1688–703. http://dx.doi.org/10.1109/49.650043.

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5

Wai-Tian Tan e A. Zakhor. "Video multicast using layered FEC and scalable compression". IEEE Transactions on Circuits and Systems for Video Technology 11, n. 3 (marzo 2001): 373–86. http://dx.doi.org/10.1109/76.911162.

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6

Basha, Sardar N., e A. Rajesh. "Scalable Video Coding Using Accordion Discrete Wavelet Transform and Tucker Decomposition for Multimedia Applications". Journal of Computational and Theoretical Nanoscience 16, n. 2 (1 febbraio 2019): 601–8. http://dx.doi.org/10.1166/jctn.2019.7777.

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Abstract (sommario):

The digital world demands the transmission and storage of high quality video for streaming and broadcasting applications, the constraints are the network bandwidth and the memory of devices for the various multimedia and scientific applications, the video consists of spatial and temporal redundancies. The objective of any video compression algorithm is to eliminate the redundant information from the video signal during compression for effective transmission and storage. The correlation between the successive frames has not been exploited enough by the current compression algorithms. In this paper, a novel method for video compression is presented. The proposed model, applies the transformation on set of group of pictures (GOP). The high spatial correlation is achieved from the spatial and temporal redundancy of GOP by accordion representation and this helps to bypass the computationally demanding motion compensation step. The core idea of the proposed technique is to apply Tucker Decomposition (TD) on the Discrete Wavelet Transform (DWT) coefficients of the Accordion model of the GOP. We use DWT to separate the video in to different sub-images and TD to efficiently compact the energy of sub-images. The blocking artifacts will be considerably eliminated as the block size is huge. The proposed method attempts to reduce the spatial and temporal redundancies of the video signal to improve the compression ratio, computation time, and PSNR. The experimental results prove that the proposed method is efficient especially in high bit rate and with slow motion videos.

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7

Zhang, Mengmeng, Hongyun Lu e Zhi Liu. "Quality Scalability Compression on Single-Loop Solution in HEVC". Mathematical Problems in Engineering 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/149859.

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This paper proposes a quality scalable extension design for the upcoming high efficiency video coding (HEVC) standard. In the proposed design, the single-loop decoder solution is extended into the proposed scalable scenario. A novel interlayer intra/interprediction is added to reduce the amount of bits representation by exploiting the correlation between coding layers. The experimental results indicate that the average Bjøntegaard delta rate decrease of 20.50% can be gained compared with the simulcast encoding. The proposed technique achieved 47.98% Bjøntegaard delta rate reduction compared with the scalable video coding extension of the H.264/AVC. Consequently, significant rate savings confirm that the proposed method achieves better performance.

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8

Bhowmik, Deepayan, e Charith Abhayaratne. "2D+t Wavelet Domain Video Watermarking". Advances in Multimedia 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/973418.

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A novel watermarking framework for scalable coded video that improves the robustness against quality scalable compression is presented in this paper. Unlike the conventional spatial-domain (t + 2D) water-marking scheme where the motion compensated temporal filtering (MCTF) is performed on the spatial frame-wise video data to decompose the video, the proposed framework applies the MCTF in the wavelet domain (2D + t) to generate the coefficients to embed the watermark. Robustness performances against scalable content adaptation, such as Motion JPEG 2000, MC-EZBC, or H.264-SVC, are reviewed for various combinations of motion compensated 2D + t + 2D using the proposed framework. The MCTF is improved by modifying the update step to follow the motion trajectory in the hierarchical temporal decomposition by using direct motion vector fields in the update step and implied motion vectors in the prediction step. The results show smaller embedding distortion in terms of both peak signal to noise ratio and flickering metrics compared to frame-by-frame video watermarking while the robustness against scalable compression is improved by using 2D + t over the conventional t + 2D domain video watermarking, particularly for blind watermarking schemes where the motion is estimated from the watermarked video.

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9

Glenn, William E., John Marcinka e Robert Dhein. "Simple Scalable Video Compression Using 3-D Subband Coding". SMPTE Journal 105, n. 3 (marzo 1996): 140–43. http://dx.doi.org/10.5594/j04649.

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10

Li, Xin. "Scalable video compression via overcomplete motion compensated wavelet coding". Signal Processing: Image Communication 19, n. 7 (agosto 2004): 637–51. http://dx.doi.org/10.1016/j.image.2004.05.006.

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11

Ouaret, Mourad, Frédéric Dufaux e Touradj Ebrahimi. "Error-resilient scalable compression based on distributed video coding". Signal Processing: Image Communication 24, n. 6 (luglio 2009): 437–51. http://dx.doi.org/10.1016/j.image.2009.02.011.

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12

HoangVan, Xiem. "Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding". Electronics 9, n. 6 (30 maggio 2020): 915. http://dx.doi.org/10.3390/electronics9060915.

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Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve high quality inter prediction, we propose a long-term reference coding method, which adaptively exploits the temporal correlation among frames in surveillance video. Second, to optimize the SSVC compression performance, we design a quantization parameter adaptation mechanism in which the relationship between SSVC rate-distortion (RD) performance and the quantization parameter is statistically modeled by a fourth-order polynomial function. Afterwards, an appropriate quantization parameter is derived for frames at long-term reference position. Experiments conducted for a common set of surveillance videos have shown that the proposed SSVC significantly outperforms the relevant SHVC standard, notably by around 6.9% and 12.6% bitrate saving for the low delay (LD) and random access (RA) coding configurations, respectively while still providing a similar perceptual decoded frame quality.

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13

de Faria Silva, Igor, Lucas Souza e Silva, Cassio Alves Carneiro, Zelia Myriam Assis Peixoto e Flavia Magalhaes Freitas Ferreira. "An FPGA-Based SIMD Architecture for Video Compression with Scalable Throughput". IEEE Potentials 35, n. 1 (gennaio 2016): 32–37. http://dx.doi.org/10.1109/mpot.2014.2312426.

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14

Leung, R., e D. Taubman. "Perceptual Optimization for Scalable Video Compression Based on Visual Masking Principles". IEEE Transactions on Circuits and Systems for Video Technology 19, n. 3 (marzo 2009): 309–22. http://dx.doi.org/10.1109/tcsvt.2009.2017078.

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15

Pande, Amit, Amit Verma, Ankush Mittal e Ashish Agrawal. "Network resource allocation of e-learning videos for scalable video delivery using content-based compression". International Journal of Signal and Imaging Systems Engineering 2, n. 3 (2009): 117. http://dx.doi.org/10.1504/ijsise.2009.033724.

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16

Ahmadpour, Sima, Tat-Chee Wan, Zohreh Toghrayee e Fariba HematiGazafi. "Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)". Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/8098574.

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Designing an effective and high performance network requires an accurate characterization and modeling of network traffic. The modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of its statistical properties and finds the best distribution using both the graphical method and the hypothesis test. The data set used in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three different movies.

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17

Yan, Tao, Xiao Xiong Zhou, Wen Ting Luo, Ze Liang Liu e Pan Dong Zhang. "Multi-View Video Coding Based on Video Correction". Advanced Materials Research 989-994 (luglio 2014): 3714–17. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3714.

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In order to enables the video to be displayed on a multitude of different terminals, Multi-view video coding (MVC) demands high compression rates as well as view scalability. A new the inter-view prediction structure with flexibility, MVC compatibility and view scalability is proposed. The proposed scheme first correct views. It then uses the position of the cameras and the relation of inter-views to divide all views into base view and enhancement views. Finally, the views quire by the viewers use scalable Multi-view Video Coding (SMVC). The main bitstream is the same as a H.264/AVC mono-sequence bitstream for H.264/AVC compatibility. The auxiliary bitstream come from enhancement views coding. We proposed SMVC scheme is tested with two Multi-view sequences to determine its flexibly view scalability, high coding efficiency and random access performance.

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18

Podilchuk, Christine I. "Streaming video and rate scalable compression: what are the challenges for watermarking?" Journal of Electronic Imaging 13, n. 1 (1 gennaio 2004): 198. http://dx.doi.org/10.1117/1.1632499.

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19

Kirenko, I. O., R. J. van der Vleuten e L. Shao. "Optimizing Scalable Video Compression for Efficient Implementation on a VLIW Media Processor". IEEE Transactions on Multimedia 9, n. 2 (febbraio 2007): 429–34. http://dx.doi.org/10.1109/tmm.2006.887996.

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20

Pao-Chi Chang e Ta-Te Lu. "A scalable video compression technique based on wavelet transform and MPEG coding". IEEE Transactions on Consumer Electronics 45, n. 3 (1999): 788–93. http://dx.doi.org/10.1109/30.793604.

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21

Khairy, Mayada, Alaa Hamdy, Amr Elsayed e Hesham Farouk. "Efficient Enhancement for Spatial Scalable Video Coding Transmission". Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8239491.

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Scalable Video Coding (SVC) is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC). In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB) mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET). Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT) across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL) and enhancement layers (ELs) and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP) and the interlayer base mode (ILBM) method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP) and the full-base mode (FBM) method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.

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22

Van der Auwera, Geert, Prasanth T. David, Martin Reisslein e Lina J. Karam. "Traffic and Quality Characterization of the H.264/AVC Scalable Video Coding Extension". Advances in Multimedia 2008 (2008): 1–27. http://dx.doi.org/10.1155/2008/164027.

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The recent scalable video coding (SVC) extension to the H.264/AVC video coding standard has unprecedented compression efficiency while supporting a wide range of scalability modes, including temporal, spatial, and quality (SNR) scalability, as well as combined spatiotemporal SNR scalability. The traffic characteristics, especially the bit rate variabilities, of the individual layer streams critically affect their network transport. We study the SVC traffic statistics, including the bit rate distortion and bit rate variability distortion, with long CIF resolution video sequences and compare them with the corresponding MPEG-4 Part 2 traffic statistics. We consider (i) temporal scalability with three temporal layers, (ii) spatial scalability with a QCIF base layer and a CIF enhancement layer, as well as (iii) quality scalability modes FGS and MGS. We find that the significant improvement in RD efficiency of SVC is accompanied by substantially higher traffic variabilities as compared to the equivalent MPEG-4 Part 2 streams. We find that separately analyzing the traffic of temporal-scalability only encodings gives reasonable estimates of the traffic statistics of the temporal layers embedded in combined spatiotemporal encodings and in the base layer of combined FGS-temporal encodings. Overall, we find that SVC achieves significantly higher compression ratios than MPEG-4 Part 2, but produces unprecedented levels of traffic variability, thus presenting new challenges for the network transport of scalable video.

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23

Heindel, Andreas, Eugen Wige e Andre Kaup. "Low-Complexity Enhancement Layer Compression for Scalable Lossless Video Coding Based on HEVC". IEEE Transactions on Circuits and Systems for Video Technology 27, n. 8 (agosto 2017): 1749–60. http://dx.doi.org/10.1109/tcsvt.2016.2556338.

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24

Secker, A., e D. Taubman. "Lifting-based invertible motion adaptive transform (LIMAT) framework for highly scalable video compression". IEEE Transactions on Image Processing 12, n. 12 (dicembre 2003): 1530–42. http://dx.doi.org/10.1109/tip.2003.819433.

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25

Gavrovska, Ana, Milan Milivojević e Goran Zajić. "Quality assessment experiment for 4k SVT-AV1 video format". Telfor Journal 13, n. 1 (2021): 35–40. http://dx.doi.org/10.5937/telfor2101035g.

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The industrial consortium AOMedia has recently developed the AV1 (AOMedia Video 1) format considered as a successor of VP9. One of the main goals with the video coding and compression format is to enable efficient Web based video content delivery, as in the case of OTT (Over-the-top) services. Nowadays, there are different AV1 software implementations available for experimental analysis besides the reference tool libaom. The Scalable Video Technology for AV1, or SVT-AV1 CODEC, is oriented towards further optimizations for specific Intel hardware. In this paper, we have performed a video quality assessment experiment with some of the SVT-AV1 implementations using 4k like video. The results show the advantages of SVT-AV1 format, as well as its performance for different quantization factors.

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26

Wai-Tian Tan e A. Zakhor. "Real-time Internet video using error resilient scalable compression and TCP-friendly transport protocol". IEEE Transactions on Multimedia 1, n. 2 (giugno 1999): 172–86. http://dx.doi.org/10.1109/6046.766738.

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27

Hongkai Xiong, Lingchen Zhu, Nannan Ma e Yuan F. Zheng. "Scalable Video Compression Framework With Adaptive Orientational Multiresolution Transform and Nonuniform Directional Filterbank Design". IEEE Transactions on Circuits and Systems for Video Technology 21, n. 8 (agosto 2011): 1085–99. http://dx.doi.org/10.1109/tcsvt.2011.2133310.

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28

Mai, Zicong, Hassan Mansour, Panos Nasiopoulos e Rabab Kreidieh Ward. "Visually Favorable Tone-Mapping With High Compression Performance in Bit-Depth Scalable Video Coding". IEEE Transactions on Multimedia 15, n. 7 (novembre 2013): 1503–18. http://dx.doi.org/10.1109/tmm.2013.2266633.

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29

Adami, N., A. Signoroni e R. Leonardi. "State-of-the-Art and Trends in Scalable Video Compression With Wavelet-Based Approaches". IEEE Transactions on Circuits and Systems for Video Technology 17, n. 9 (settembre 2007): 1238–55. http://dx.doi.org/10.1109/tcsvt.2007.906828.

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30

Mrak, M., T. Zgaljic e E. Izquierdo. "Influence of downsampling filter characteristics on compression performance in wavelet-based scalable video coding". IET Image Processing 2, n. 3 (2008): 116. http://dx.doi.org/10.1049/iet-ipr:20070185.

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31

Talluri, R., K. Oehler, T. Barmon, J. D. Courtney, A. Das e J. Liao. "A robust, scalable, object-based video compression technique for very low bit-rate coding". IEEE Transactions on Circuits and Systems for Video Technology 7, n. 1 (1997): 221–33. http://dx.doi.org/10.1109/76.554433.

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32

Fradj, Bilel Ben, e Azza Ouled Zaid. "Scalable video coding using motion-compensated temporal filtering and intra-band wavelet based compression". Multimedia Tools and Applications 69, n. 3 (6 luglio 2012): 1089–109. http://dx.doi.org/10.1007/s11042-012-1170-5.

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33

K, Raghu. "Scalable Video Compression by Employing TEMPO-SPA Arrangement along with Combined ADCT, Retaining-RLE Method". International Journal of Computer Applications 88, n. 11 (14 febbraio 2014): 20–24. http://dx.doi.org/10.5120/15397-3922.

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34

Bachu, Srinivas, e N. Ramya Teja. "Fuzzy Holoentropy-Based Adaptive Inter-Prediction Mode Selection for H.264 Video Coding". International Journal of Mobile Computing and Multimedia Communications 10, n. 2 (aprile 2019): 42–60. http://dx.doi.org/10.4018/ijmcmc.2019040103.

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Abstract (sommario):

Due to the advancement of multimedia and its requirement of communication over the network, video compression has received much attention among the researchers. One of the popular video codings is scalable video coding, referred to as H.264/AVC standard. The major drawback in the H.264 is that it performs the exhaustive search over the interlayer prediction to gain the best rate-distortion performance. To reduce the computation overhead due to exhaustive search on mode prediction process, this paper presents a new technique for inter prediction mode selection based on the fuzzy holoentropy. This proposed scheme utilizes the pixel values and probabilistic distribution of pixel symbols to decide the mode. The adaptive mode selection is introduced here by analyzing the pixel values of the current block to be coded with those of a motion compensated reference block using fuzzy holoentropy. The adaptively selected mode decision can reduce the computation time without affecting the visual quality of frames. Experimentation of the proposed scheme is evaluated by utilizing five videos, and from the analysis, it is evident that proposed scheme has overall high performance with values of 41.367 dB and 0.992 for PSNR and SSIM respectively.

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35

CHIANG, TIHAO, e YA-QIN ZHANG. "STEREOSCOPIC VIDEO CODING USING A FAST AND ROBUST AFFINE MOTION SEARCH". International Journal of Image and Graphics 01, n. 02 (aprile 2001): 231–50. http://dx.doi.org/10.1142/s0219467801000153.

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This paper presents a stereoscopic video compression scheme using a novel fast affine motion estimation technique. A temporal scalable approach is used to achieve backward compatibility with a standard definition TV. We use an adaptive mode selection scheme from three temporal locations in both channels. Both block-based and affine-motion based approaches are used to achieve two levels of improvements with different complexities. An innovative motion estimation technique using Gauss–Newton optimization and pyramid processing is implemented to efficiently estimate affine parameters. Unlike other Gauss–Newton approaches, our search technique uses only addition, subtraction and multiplication and it converges within four iterations, which implies great complexity reduction. An efficient and robust affine motion prediction yields significant over the disparity-based approach. Part of the disparity-based approach has been tested in the rigorous MPEG-2 bitstream exchange process, and adopted in the MPEG-2 Multi-View Profile (MVP).

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36

Jiang, Hong, Chengbo Li, Raziel Haimi-Cohen, Paul A. Wilford e Yin Zhang. "Scalable Video Coding Using Compressive Sensing". Bell Labs Technical Journal 16, n. 4 (marzo 2012): 149–69. http://dx.doi.org/10.1002/bltj.20539.

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37

Li, Yong, Wenrui Dai, Junni Zou, Hongkai Xiong e Yuan F. Zheng. "Scalable Structured Compressive Video Sampling With Hierarchical Subspace Learning". IEEE Transactions on Circuits and Systems for Video Technology 30, n. 10 (ottobre 2020): 3528–43. http://dx.doi.org/10.1109/tcsvt.2019.2939370.

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38

Thi Hue, Le Dao, Luong Pham Van, Duong Dinh Trieu e Xiem HoangVan. "Efficient and Low Complexity Surveillance Video Compression using Distributed Scalable Video Coding". VNU Journal of Science: Computer Science and Communication Engineering 34, n. 1 (27 settembre 2018). http://dx.doi.org/10.25073/2588-1086/vnucsce.198.

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Video surveillance has been playing an important role in public safety and privacy protection in recent years thanks to its capability of providing the activity monitoring and content analyzing. However, the data associated with long hours surveillance video is huge, making it less attractive to practical applications. In this paper, we propose a low complexity, yet efficient scalable video coding solution for video surveillance system. The proposed surveillance video compression scheme is able to provide the quality scalability feature by following a layered coding structure that consists of one or several enhancement layers on the top of a base layer. In addition, to maintain the backward compatibility with the current video coding standards, the state-of-the-art video coding standard, i.e., High Efficiency Video Coding (HEVC), is employed in the proposed coding solution to compress the base layer. To satisfy the low complexity requirement of the encoder for the video surveillance systems, the distributed coding concept is employed at the enhancement layers. Experiments conducted for a rich set of surveillance video data shown that the proposed surveillance - distributed scalable video coding (S-DSVC) solution significantly outperforms relevant video coding benchmarks, notably the SHVC standard and the HEVC-simulcasting while requiring much lower computational complexity at the encoder which is essential for practical video surveillance applications.

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"Bit Rate Transcoding for High Efficiency Video Coding". International Journal of Engineering and Advanced Technology 9, n. 2 (30 dicembre 2019): 581–84. http://dx.doi.org/10.35940/ijeat.a1135.129219.

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High efficiency video coding (HEVC) has demonstrated a notable increase in compression performance and is taken as a successor to H.264/AVC. Efficient bit rate adaptation algorithms are required to contain the HEVC standard between real life community facilities. A present issue of bit rate transcoding is its high computational complexity which is related with the encoder of a cascaded pixel domain transcoder. This paper gives Top to Bottom (T2B) approach to reduce complexity by using different complexity schemes. Proposed approach is effective in reducing complexity in Coding Unit (CU) optimization level. Coding Unit has been analyzed in T2B Approach. While examining the coding unit information of the input video is turned to account for decreasing the number of evaluation and early terminate the process. For the Prediction Unit (PU) level the units are powerfully chosen contingent upon likelihood of Prediction Unit sizes and co-found input prediction partitioning. By utilizing this approach, complexity scalable bit rate transcoding has achieved. Machine learning approach can be used to control computational complexity. Additionally, the T2B strategy is able to gain a spread on trade-offs in transrating complexity and coding performance. Using T2B approach 15% encoding time saving is accomplished. From this scheme, for the less resolution video 27% time saving has achieved.

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Boufounos, Petros T., Hassan Mansour, Shantanu Rane e Anthony Vetro. "Dimensionality reduction of visual features for efficient retrieval and classification". APSIPA Transactions on Signal and Information Processing 5 (2016). http://dx.doi.org/10.1017/atsip.2016.14.

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Visual retrieval and classification are of growing importance for a number of applications, including surveillance, automotive, as well as web and mobile search. To facilitate these processes, features are often computed from images to extract discriminative aspects of the scene, such as structure, texture or color information. Ideally, these features would be robust to changes in perspective, illumination, and other transformations. This paper examines two approaches that employ dimensionality reduction for fast and accurate matching of visual features while also being bandwidth-efficient, scalable, and parallelizable. We focus on two classes of techniques to illustrate the benefits of dimensionality reduction in the context of various industrial applications. The first method is referred to as quantized embeddings, which generates a distance-preserving feature vector with low rate. The second method is a low-rank matrix factorization applied to a sequence of visual features, which exploits the temporal redundancy among feature vectors associated with each frame in a video. Both methods discussed in this paper are also universal in that they do not require prior assumptions about the statistical properties of the signals in the database or the query. Furthermore, they enable the system designer to navigate a rate versus performance trade-off similar to the rate-distortion trade-off in conventional compression.

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