1. Introduction
2. Related Works
2.1. HEVC Standard
2.1.1. HEVC Entropy Coding
2.1.2. Intra Prediction Modes
2.1.3. Tiles in HEVC
2.2. Selective Video Encryption
3. Proposed Video Encryption System
3.1. Intra Prediction Parameters Encryption
Algorithm 1 Encryption of IPMs. 

3.2. CABAC Level Encryption
3.3. Encryption System Based on Chaos
4. ROI Encryption Implementation in HEVC Codec
4.1. ROI Encryption System Based on Tiles
4.2. Encryption Propagation in Inter Video Coding
 1.
 The MVs of the predicted block are restricted to point only to the colocated tile of the reference frame.
 2.
 The inloop filters are disabled across the tile boundaries.
5. Results and Discussion
5.1. Experiments
5.2. Objective Measurements
5.2.1. Video Quality Metrics
5.2.2. BDBR Rate Evaluation
5.2.3. Encryption Quality
5.3. Entropy Analysis
5.3.1. Key Security
5.3.2. Visual Analysis
5.4. Subjective Evaluations
5.4.1. Design and Procedure
5.4.2. Data Processing
5.4.3. Subjective Scores
5.4.4. Statistical Analysis
5.5. Complexity Evaluations
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
 1.
 The worst case of the original frame relating to the encryption solution is at low entropy configuration. The whole image has the same color, as an example all pixels are black or blue. Thus, the total number of occurrences of the pixel ${Z}_{1}$ in the original frame P is ${H}_{{Z}_{1}}\left(P\right)=h\times w$, where ${Z}_{1}\in \{0,255\}$. In addition, the total number of occurrences of the pixel ${Z}_{2}$ (${Z}_{2}$ is any pixel except ${Z}_{1}$) in the original (no encrypted) frame P is ${H}_{{Z}_{2}}\left(P\right)=0$, where ${Z}_{2}\in \{0,255\}$ and ${Z}_{2}\ne {Z}_{1}$.
 2.
 The most secure method should generate a ciphered frame in which all pixels are randomly distributed. Therefore, the total number of occurrences of any pixel Z in the ciphered frame is ${H}_{Z}\left(C\right)=\frac{h\times w}{256}$, where $Z\in \{0,255\}$.
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Number of Bits  Code  Coded Mode 

2  10  IPM0 
3  110  IPM1 
3  111  IPM2 
000000  
6  ⋮  32 remaining IPMs 
011111 
Chroma IPM  Luma IPM  

0  26  10  1  
Planar (mode 0)  34  0  0  0 
Angular (mode 26)  26  34  26  26 
Angular (mode 10)  10  10  34  10 
DC (mode 1)  1  1  1  34 
Derived (luma mode)  0  26  10  1 
Test  P_Value  Proportion 

Frequency test  0.637  100.000 
Blockfrequency test  0.956  98.000 
Cumulativesums test  0.715  99.500 
Runs test  0.720  98.000 
Longestrun test  0.055  98.000 
Rank test  0.554  99.000 
FFT test  0.109  100.000 
nonperiodictemplates  0.546  98.973 
overlappingtemplates  0.2256  99.000 
universal  0.994  99.000 
approximty entropie  0.575  99.000 
randomexcursions  0.428  97.581 
randomexcursionsvariant  0.428  98.925 
serial test  0.519  99.000 
linearcomplexity  0.740  98.000 
Sequence  Class  Resolution  Frame Rate 

PeopleOnStreet  A  $2560\times 1600$  30 
Kimono  B  $1920\times 1080$  24 
ParkScene  B  $1920\times 1080$  24 
BasketballDrive  B  $1920\times 1080$  50 
Cactus  B  $1920\times 1080$  50 
BQTerrace  B  $1920\times 1080$  60 
Vidyo1  E  $1280\times 720$  60 
Vidyo3  E  $1280\times 720$  60 
Vidyo4  E  $1280\times 720$  60 
Sequence  Original  Encrypted ROI  

PSNR  SSIM  PSNR  SSIM  
PeopleOnStreet  42.7  0.92  11.1  0.22 
Kimono1  42.1  0.95  9.8  0.22 
ParkScene  42.2  0.90  10.76  0.21 
Cactus  42.4  0.93  10.3  0.21 
BQTerrace  41.7  0.90  10.7  0.22 
BasketballDrive  41.4  0.95  10.0  0.22 
Vidyo1  45.3  0.91  11.2  0.20 
Vidyo3  44.5  0.93  10.8  0.20 
Vidyo4  44.7  0.90  11.0  0.21 
Sequence  QP  OriginalPSNR  SEPSNR  OriginalSSIM  SESSIM  

Y  U  V  Y  U  V  Y  U  V  Y  U  V  
BasketballDrive (B)  22  42.1  43.5  44.9  10.2  10.8  11.1  0.92  0.94  0.94  0.21  0.22  0.24 
27  41.3  42.2  43.6  10.1  10.7  11.0  0.89  0.91  0.93  0.2  0.21  0.21  
32  37.5  38.8  39.1  9.8  10.5  10.9  0.76  0.72  0.88  0.16  0.18  0.22  
37  36.7  37.9  38.1  8.1  8.9  10.1  0.74  0.78  0.81  012  0.16  0.18  
Kimono1 (B)  22  43.7  44.1  45.1  9.5  10.1  10.3  0.96  0.96  0.99  0.17  0.18  0.19 
27  42.3  42.9  43.1  9.1  10.0  10.1  0.95  0.98  0.98  0.17  0.17  0.19  
32  38.8  38.9  39.9  8.5  9.9  10.3  0.82  0.83  0.88  0.14  0.16  0.18  
37  37.8  38.6  38.9  7.5  8.4  9.9  0.77  0.78  0.80  0.12  0.14  0.17  
PeopleOnStreet (A)  22  38.6  41.4  43.4  10.2  10.6  11.3  0.95  0.95  0.97  0.19  0.19  0.22 
27  38.3  39.8  41.2  9.5  9.9  10.3  0.91  0.93  0.94  0.17  0.20  0.22  
32  37.0  38.1  40.6  8.9  9.3  10.1  0.88  0.90  0.92  0.18  0.19  0.21  
37  35.4  37.6  38.9  8.1  9.0  9.8  0.78  0.83  0.88  0.15  0.15  0.19 
Sequence  Wallendael et al. [5]  Boyadjis et al. [22]  Proposed SE  

PSNR  SSIM  PSNR  SSIM  PSNR  SSIM  
BasketballDrive  11.4  0.40  10.4  0.43  9.9  0.17 
Kimono1  10.1  0.32  6.6  0.27  8.9  0.14 
Vidyo1  12.9  0.61  11.2  0.55  10.1  0.18 
Class  Main Intra  Random Access  

TC, TCs, MV and MVs  All  TC, TCs, MV and MVs  All  
B  11.1  10.2  10.4  10.2 
D  9.3  8.9  8.7  8.4 
E  10.2  9.6  9.7  9.1 
Class  Main Intra  Random Access  

TC, TCs, MV and MVs  All  TC, TCs, MV and MVs  All  
B  0.33  0.25  0.30  0.21 
D  0.26  0.20  0.23  0.18 
E  0.22  0.19  0.20  0.17 
Resolution  Sequence  Intra Coding ($4\times 4$ Tiles)  Inter Coding ($4\times 4$ Tiles)  

Bit Rate Loss (%)  Complexity Increase (%)  Bit Rate Loss (%)  Complexity Increase (%)  
BDRate  Encoding  Decoding  BDRate  Encoding  Decoding  
$2560\times 1600$  PeopleOnStreet  3.67  3.05  1.87  5.13  3.27  2.88 
$1920\times 1080$  Kimono1  5.16  3.16  1.21  13.19  3.87  1.96 
ParkScene  4.09  2.34  1.13  9.81  3.08  1.89  
Cactus  5.43  2.82  2.02  7.65  3.96  2.19  
BQTerrace  7.18  2.19  1.67  18.23  3.54  1.93  
BasketballDrive  6.34  3.16  2.15  17.11  3.78  2.44  
$1280\times 720$  Vidyo1  4.21  2.13  1.32  13.87  2.60  1.91 
Vidyo3  6.17  2.31  1.41  10.08  2.98  2.07  
Vidyo4  6.01  2.25  1.48  15.91  2.71  1.88  
Average  5.36  2.60  1.58  12.33  3.31  2.12 
Resolution  Sequence  Intra Coding ($4\times 3$ Tiles)  Inter Coding ($4\times 3$ Tiles)  

Bit Rate Loss (%)  Complexity Increase (%)  Bit Rate Loss (%)  Complexity Increase (%)  
BDRate  Encoding  Decoding  BDRate  Encoding  Decoding  
$2560\times 1600$  PeopleOnStreet  2.14  2.11  1.12  3.42  2.16  1.71 
$1920\times 1080$  Kimono1  4.13  2.13  1.01  11.65  2.48  1.63 
ParkScene  3.68  1.98  1.06  8.55  2.18  1.12  
Cactus  3.14  1.68  1.22  5.12  2.56  1.67  
BQTerrace  4.32  1.67  1.10  12.56  2.14  1.73  
BasketballDrive  4.74  1.36  1.17  13.49  2.68  1.41  
$1280\times 720$  Vidyo1  2.08  1.43  1.15  9.19  1.93  1.43 
Vidyo3  4.65  1.21  1.08  7.81  1.68  1.47  
Vidyo4  4.79  1.64  1.33  11.02  1.98  1.39  
Average  3.74  1.69  1.13  9.20  2.19  1.50 
Sequence  EQ in [32]  EQ of Proposed SE 

Kimono1  8996  $\mathbf{10192}$ 
PeopleOnStreet  14884  $\mathbf{18965}$ 
Vidyo1  −  $\mathbf{4288}$ 
Vidyo3  −  $\mathbf{4319}$ 
Vidyo4  −  $\mathbf{4380}$ 
Sequence  Information Entropy 

PeopleOnStreet  7.10 
Kimono  7.23 
ParkScene  7.50 
BasketballDrive  7.44 
Cactus  7.35 
BQTerrace  7.21 
Vidyo1  7.01 
Vidyo3  7.34 
Vidyo4  7.21 
Algorithm  Encrypted Elements  Format Compliant  Bit Increase  Encryption Algorithm 

Xu [37]  IPM, MVDs, T1s, signs of the NZ coefficients  yes  no  Chaos 
Abomhara [38]  I frame  no  no  AES 
Shahid [19]  T1s, NZ level  yes  no  AES 
Fei [23]  IPM, MVD, Signs of residual  yes  yes  Chaos 
Sung [39]  Motion vector  yes  yes  RC4 
Wei [40]  NALUs  yes  yes  RC4 
Wang [41]  IPM, MVD, Quantization coefficients  yes  yes  Hash and AES 
Shuli [42]  IPM, MVDs, Signs of residual, delta QP  yes  yes  Chaos and AES 
Proposed algorithm  IPM, MV, MVS, TC, TCS  yes  IPM  Chaos 
Source  DF  FValue  PValue 

Class  2  1.0121  0.4001 
Content  4  0.9871  0.5501 
QP  3  0.1281  0.128 
SE Scheme  1  97.754  <0.0001 
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