In view of the fact that the current high efficiency video coding standard does not consider the characteristics of human vision, this paper proposes a perceptual video coding algorithm based on the just noticeable distortion model (JND). The adjusted JND model is combined into the transformation quantization process in high efficiency video coding (HEVC) to remove more visual redundancy and maintain compatibility. First of all, we design the JND model based on pixel domain and transform domain respectively, and the pixel domain model can give the JND threshold more intuitively on the pixel. The transform domain model introduces the contrast sensitive function into the model, making the threshold estimation more precise. Secondly, the proposed JND model is embedded in the HEVC video coding framework. For the transformation skip mode (TSM) in HEVC, we adopt the existing pixel domain called nonlinear additively model (NAMM). For the non-transformation skip mode (non-TSM) in HEVC, we use transform domain JND model to further reduce visual redundancy. The simulation results show that in the case of the same visual subjective quality, the algorithm can save more bitrates.
Sullivan, G.J., Ohm, J., Han, W.J. and Wiegand, T. (2012) Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Transaction on Circuits & Systems for Video Technology, 21, 1649-1668.
Kim, J., Bae, S.H. and Kim, M. (2015) An HEVC-Compliant Perceptual Video Coding Scheme Based on JND Models for Variable Block-Sized Transform Kernels. IEEE Transactions on Circuits and Systems for Video Technology, 25, 1786-1800.
Ki, S., Bae, S.H., Kim, M. and Ko, H. (2018) Learning-Based Just Noticeable Quantization Distortion Modeling for Perceptual Video Coding. IEEE Transactions on Image Processing, 27, 3178-3193. https://doi.org/10.1109/TIP.2018.2818439
Wang, H. (2016) MCL-JCV: A JND-Based H.264/AVC Video Quality Assessment Dataset. 2016 IEEE International Conference on Image Processing, 25-28 September 2016, Phoenix, AZ, 1509-1513. https://doi.org/10.1109/ICIP.2016.7532610
Chou, C.H. and Li, Y.C. (1995) A Perceptual Tuned Sub-Band Image Coder Based on the Measure of Just-Noticeable-Distortion Profile. IEEE Transaction on Circuits & Systems for Video Technology, 5, 467-476. https://doi.org/10.1109/76.475889
Yang, X.K., Ling, W.S., Lu, Z.K., Ong, E.P. and Yao, S.S. (2005) Just Noticeable Distortion Model and Its Applications in Video Coding. Signal Processing Image Communication, 20, 662-680. https://doi.org/10.1016/j.image.2005.04.001
Liu, A., Lin, W., Paul, M., Deng, C. and Zhang, F. (2010) Just Noticeable Difference for Images with Decomposition Model for Separating Edge and Textured Regions. IEEE Transactions on Circuits & Systems for Video Technology, 20, 1648-1652.
Wu, J.J., Qi, F. and Shi, M. (2012) Self-Similarity Based Structural Regularity for Just Noticeable Difference Estimation. Journal of Visual Communication and Image Representation, 23, 845-852. https://doi.org/10.1016/j.jvcir.2012.04.010
Ahumada Jr., A.J. and Peterson, H.A. (1992) Luminance-Model-Based DCT Quantization for Color Image Compression. Human Vision, Visual Processing, & Digital Display III, 1666. https://doi.org/10.1117/12.135982
Watson, A.B. (1993) A Technique for Visual Optimization of DCT Quantization Matrices for Individual Images. Society for Display Digest of Technical Papers XXIV, 9, 946-949. https://doi.org/10.2514/6.1993-4512
Wei, Z. and Ngan, K.N. (2009) Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain. IEEE Transaction on Circuits and Systems for Video Technolo-gy, 19, 337-346.
Wu, J., Shi, G., Lin, W., Liu, A. and Qi, F. (2013) Just Noticeable Difference Estimation for Images with Free-Energy Principle. IEEE Transactions on Multimedia, 15, 1705-1710. https://doi.org/10.1109/TMM.2013.2268053
Chen, Z. and Guillemot, C. (2010) Perceptually-Friendly H.264/AVC Video Coding Based on Just-Noticeable-Distortion Model. IEEE Transactions on Circuits and Systems for Video Technology, 20, 806-819.