Abstract
Texture and depth images are generally used for 3D viewing with advanced displays. Because sthe characteristics of a depth image are very different from those of a texture image, an efficient compression method is required to transmit a depth image in a limited bandwidth. In this paper, a low-complexity two-step lossless depth coding (LTLDC) method using coarse lossy coding is proposed. The proposed method downsamples an original image and then coarsely compresses the downsampled image in the first step. This compressed image is upsampled, and then its residual is generated by subtracting the upsampled image from the original image. In the second step, each coding block within the residual and original images is adaptively compressed with a fast mode decision method in a lossless way, and the proposed method determines the best block based on their coding performance. Experimental results show that the proposed LTLDC method achieves a bitrate reduction of 4.35% with encoding complexity reduction of 20.38%.
Similar content being viewed by others
References
Bossen F (2013) “Common test conditions and software reference configurations,” Doc. JCTVC-L110.
Budagavi M, Fuldseth A, Bjøntegaard G, Sze V, Sadafale M (2013) Core transform Design in the High Efficiency Video Coding (HEVC) standard. IEEE J Selected Topics Signal Process 7(6):1029–1041
Chen F, Xing Q, Liu F (2020) Technology of Hiding and Protecting the secret image based on Two-Channel deep hiding network. IEEE Access 8:21966–21979
Dai T, Cai J, Zhang Y, Xia S-T, Zhang L (2019) Second-order attention network for single image super-resolution. IEEE Conf Comput Vis Pattern Recognit:11065–11074
Fehn C (2004) “Depth-image-based rendering (DIBR), compression and transmission for a new approach on 3D-TV,” SPIE Stereoscopic Displays and Virtual Reality Systems XI
Hertel DW, Chang E (2007) Image quality standards in automotive vision applications. IEEE Intell Vehicles Symp
ISO/IEC 15444–2:2004—Information Technology—JPEG (2000) Image coding system: extensions. 2009
K. Kazui, T. Kubota, K. Takeuchi, A.Nakagawa (2015) Proposal on new HEVC profile for hierarchical lossless coding. ISO/IEC JTC1/SC29/WG11, Doc. M35785
Kim S-H, Kang J-W, Kuo C-CJ (2011) Improved H.264/AVC lossless intra coding with two-layered residual coding (TRC). IEEE Trans Circuits and Syst Video Technol 21(7):1005–1010
Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. IEEE Conf. Comput. Vis. Pattern Recognit.:1646–1654
Lee JY, Park HW (2020) HEVC-based three-layer texture and depth coding for lossless synthesis in 3D video coding. Multimed Tools Appl 79(29–30):20929–20945
Lee JY, Lin J-L, Chen Y-W, Chang Y-L, Kovliga I, Fartukov A, Mishurosvskiy M, Wey H-C, Huang Y-W, Lei S (2015) Depth-based texture coding in AVC-compatible 3D video coding. IEEE Trans. Circuits and Systems for Video Technol 25(8):1347–1361
J. Y. Lee, Y. Choi, W. Lim, G. Bang (2020) AHG11: Deep neural network for super-resolution. ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29, Doc. JVET-T0096
Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. IEEE Conf. Comput. Vis. Pattern Recognit.:136–144
K. Müller, Vetro A. (2014) Common Test Conditions of 3DV Core Experiments. ISO/IEC and ITU-T, Doc. JCT3V-G1100
Nguyen T, Helle P, Winken M, Bross B, Marpe D, Schwarz H, Wiegand T (2013) Transform coding techniques in HEVC. IEEE J Selected Topics Signal Process 7(6):978–989
Niu B, Wen W, Ren W, Zhang X, Yang L, Wang S, Zhang K, Cao X, Shen H (2020) Single image super-resolution via a holistic attention network. Eur Conf Comp Vision:191–207
Ortega A, Ranchandra K (1988) Rate-distortion methods for image and video compression. IEEE Signal Process Mag 15(6):23–50
Pan Z, Shen H, Lu Y, Li S, Yu N (2013) A low-complexity screen compression scheme for interactive screen sharing. IEEE Trans Circ Syst Video Technol 23(6):949–960
S. A. Parah, F. Ahad, J. A. Sheikh, G. M. Bhat (2017) Hiding Clinical Information in Medical Images: A New High Capacity and Reversible Data Hiding Technique. 66, 214–230
Patel R, Lad K, Patel M, Desai M (2021) A hybrid DST-SBPNRM approach for compressed video steganography. Multimedia Syst 27(3):417–428
Patel R, Lad K, Patel M (2021) Study and investigation of video steganography over uncompressed and compressed domain: a comprehensive review. Multimedia Syst. https://doi.org/10.1007/s00530-021-00763-z
Saxena A, Fernandes FC (2013) DCT/DST-based transform coding for intra prediction in image/video coding. IEEE Trans Image Process 22(10):3974–3981
Sullivan GJ, Ohm J-R, Han W-J, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circ Syst Video Technol 22(12):1649–1668
Sullivan GJ, Boyce JM, Chen Y, Ohm J-R, Segall CA, Vetro A (2013) Standardized extensions of high efficiency video coding (HEVC). IEEE J Selected Topics Signal Process 7(6):1001–1016
T.87 : Information Technology – Lossless and Near-Lossless Compression of Continuous-Tone Still Images-Baseline, ISO-14495-1/ITU-T.87 (JPEG-LS), 2011.
Y. H. Tan, C. Yeo, Z. Li (2013) Residual DPCM for lossless coding in HEVC,” IEEE International Conference on Acoustics, Speech and Signal Processing
Wang L, Wang Y, Liang Z, Lin Z, Yang J, An W, Guo Y (2019) Learning parallax attention for stereo image super-resolution. IEEE Conf. Comput. Vis. Pattern Recognit:12250–12259
Wang Z, Chen J, Hoi SCH (2021) Deep learning for image super-resolution: a survey. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.2982166
O. Watanabe, H. Kobayashi, H. Kiya 2018 Lossless two-layer coding using histogram packing technique for HDR images. IEEE Int Symp Circ Syst
Wiegand T, Sullivan GJ, Bjntegaard G, Luthra A (2013) Overview of the H.264/AVC video coding standard. IEEE Trans. Circ SystVideo Technol 13(7):560–576
Wige E, Yammine G, Amon P, Hutter A, Kaup A (2013) Pixel-based averaging predictor for HEVC lossless coding. IEEE Int Conf Image Process
Yang S, Li B, Song Y, Xu J, Lu Y (2018) A hardware-accelerated system for high resolution real-time screen sharing. IEEE Trans Circ Syst Video Technol 29(3):881–891
Yang Y, Xiao X, Cai X, Zhang W (2020) A secure and privacy-preserving technique based on contrast-enhancement reversible data hiding and plaintext encryption for medical images. IEEE Signal Process Lett 27:256–260
Yang K, Suzuki T, Yoshida T (2020) Two-layer lossless coding of HDR images specialized for radiance format. APSIPA Annual Summit and Conference
Yoshida T, Iwahashi M, Kiya H (2018) Two-layer lossless coding for high dynamic range images based on range compression and adaptive inverse tone-mapping. IEICE Trans Fundamentals 101(1):259–266
Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. IEEE Conf. Comput. Vis. Pattern Recognit.:2472–2481
Zhao H, Kong X, He J, Qiao Y, Dong C (2020) Efficient image super-resolution using pixel attention. Eur Conf Computer Vision:56–72
Zhou M, Gao W, Jiang M, Yu H (2012) HEVC lossless coding and improvements. IEEE Trans. Circ Syst Video Technol 22(12):1839–1843
Zuo Y, Wu Q, Fang Y, An P, Huang L, Chen Z (2020) Multi-scale frequency reconstruction for guided depth map super-resolution via deep residual network. IEEE Trans Circuits Syst Video Technol 30(2):297–306
Acknowledgments
This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (IITP-2021-0-02067) and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2020M3F6A1109603, NRF-2021R1C1C1006459, NRF-2021R1F1A1060816).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
It has not been published elsewhere and that it has not been submitted simultaneously for publication elsewhere.
Rights and permissions
About this article
Cite this article
Lee, J.Y., Van Le, T., Choi, Y. et al. Low-complexity two-step lossless depth coding using coarse Lossy coding. Multimed Tools Appl 81, 14065–14079 (2022). https://doi.org/10.1007/s11042-022-12145-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12145-2