# Sample-Based Gradient Edge and Angular Prediction for VVC Lossless Intra-Coding
## 5 Conclusions  
Lossless coding has great application prospects in some fields that require high video quality. However, the low compression efficiency of current VVC lossless coding greatly limits the practical application of lossless coding. In order to improve the coding performance of VVC lossless coding, this paper analyzes the characteristics of several types of frequently occurring image regions and proposes an SGAP method under the framework of sample-based prediction. In lossless intra-coding, SGAP can perform accurate prediction for edge regions, smooth regions with less noise and directional texture regions and provide  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Sequence** & **BDPCM** & **SAP** & **SAP1** & **SAP\(-\)E** & **LIP\(-\)RMED** & **SGAP** \\ \hline \multirow{4}{*}{D} & BasketballPass & \(-\)8.01 & \(-\)9.45 & \(-\)9.45 & \(-\)12.04 & \(-\)8.70 & \(\mathbf{-13.08}\) \\  & BlowingBubbles & \(-\)2.90 & \(-\)4.89 & \(-\)4.89 & \(-\)5.60 & \(-\)2.97 & \(\mathbf{-5.95}\) \\  & BQSquare & \(-\)2.01 & \(-\)2.49 & \(-\)2.49 & \(-\)3.18 & \(-\)2.31 & \(\mathbf{-3.57}\) \\  & RaceHorses & \(-\)4.39 & \(-\)6.56 & \(-\)6.56 & \(-\)9.58 & \(-\)7.13 & \(\mathbf{-10.47}\) \\ \hline \multirow{4}{*}{E} & FourPeople & \(-\)6.29 & \(-\)7.52 & \(-\)7.52 & \(-\)10.28 & \(-\)8.09 & \(\mathbf{-11.20}\) \\  & Johnny & \(-\)4.92 & \(-\)6.31 & \(-\)6.31 & \(-\)8.10 & \(-\)6.22 & \(\mathbf{-8.81}\) \\  & KristenAndSara & \(-\)5.64 & \(-\)6.86 & \(-\)6.85 & \(-\)8.80 & \(-\)6.58 & \(\mathbf{-9.56}\) \\ \hline \multirow{4}{*}{F} & BasketballDnilText & \(-\)1.93 & \(-\)4.48 & \(-\)4.48 & \(-\)4.92 & \(-\)2.78 & \(\mathbf{-5.72}\) \\  & ChinaSpeed & \(-\)11.42 & \(-\)12.47 & \(-\)12.48 & \(-\)13.93 & \(-\)9.22 & \(\mathbf{-15.03}\) \\ \cline{1-1}  & SlideEditing & \(-\)10.47 & \(-\)8.26 & \(-\)8.28 & \(-\)10.05 & \(-\)9.88 & \(\mathbf{-10.89}\) \\ \cline{1-1}  & SlideShow & \(-\)11.09 & \(-\)11.24 & \(-\)11.26 & \(-\)16.42 & \(-\)13.67 & \(\mathbf{-17.82}\) \\ \hline \hline \multicolumn{2}{c}{Average of \(\Delta\)bit\(-\)rate} & \(-\)3.88 & \(-\)4.84 & \(-\)4.84 & \(-\)6.62 & \(-\)5.15 & \(\mathbf{-7.31}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Encoding Time** & **Decoding Time** \\ \hline BDPCM & 124.1 & 94.6 \\ SAP & 98.9 & 75.0 \\ SAP1 & 98.3 & 74.8 \\ SAP-E & 97.4 & 71.4 \\ LIP-RMED & 131.2 & 92.7 \\ SGAP & 105.4 & 74.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of average encoding and decoding time (%) of optimization coding schemes compared to basic VVC coding.  
significant coding performance gain. The experimental results show that SGAP can save 7.31% coding bit rate on average at the cost of increasing the lossless encoding time in VVC lossless intra-coding. Compared with existing lossless coding optimization methods, SGAP has a significant advantage[PERSON]  
Conceptualization, G.C. and M.L.; methodology, G.C. and M.L.; software, G.C.; validation, G.C. and M.L.; formal analysis, G.C. and M.L.; investigation, G.C.; resources, G.C.; data curation, G.C.; writing--original draft preparation, G.C.; writing--review and editing, G.C. and M.L.; visualization, G.C.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of China through the National Key Research and Development Program of China, under Grant 2019 YFB2204500.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflicts of interest.