Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics
Abstract
:1. Introduction
- A novel technique based on the mean predictive block value is proposed to manage computational complexity in lossless and lossy BMAs.
- It is shown that the proposed method offers high resolution predicted frames for low, medium, and high motion activity videos.
- For lossless prediction, the proposed algorithm speeds up the search process and efficiently reduces the computational complexity. In this case, the performance of the proposed technique is evaluated using the mean value of two motion vectors for the above and left previous neighboring macroblocks to determine the new search window. Therefore, there is a high probability that the new search window will contain the global minimum, using the partial distortion elimination (PDE) algorithm.
- For lossy block matching, previous spatially neighboring macroblocks are utilized to determine the initial search pattern step size. Seven positions are examined in the first step and five positions later. To speed up the search process, the PDE algorithm is applied.
2. Fast Block Matching Algorithms
2.1. Fixed Set of Search Patterns
2.2. Predictive Search
2.3. Partial Distortion Elimination Algorithm
3. Mean Predictive Block Matching Algorithms for Lossless (MPBMLS) and Lossy (MPBMLY) Compression
Algorithm 1 Neighboring Macroblocks. |
1: Let MB represents the current macroblock, while MBSet = {S|S ∈ video frames} 2: ForEach MB in MBSet 3: Find A and L where 4: - A & L ∈ MBSet 5: - A is the top motion vector (MVA) 6: - L is the left motion vector (MVL) 7: Compute the SI where 8: - SI is the size of MB = N × N (number of pixels) 9: End Loop |
Algorithm 2 Finding the new search window |
1: Let MB represents cthe urrent macroblock in MBSet 2: ForEach MB in MBSet: 3: Find x, y as: 4: 5: 6: Such that x, y ≤ , where is the search window size for the Full Search algorithm 7: Find NW representing the set of all points at the corners of the new search window rectangle as: 8: 9: End Loop |
Algorithm 3 Proposed MPBM technique | |
Step 1 | 1: Let s = ∑SADcenter (i.e., midpoint of the current search) and Th a pre-defined threshold value. 2: IF s < Th: 3: No motion can be found 4: Process is completed. 5: END IF |
Step 2 | 1: IF MB is in top left corner: 2: Search 5 LPS points 3: ELSE: 4: - MVA and MVL will be added to the search 5: - Use MVA and MVL for predicting the step size as: where step size = max{ Lx, Ly}. 6: - Matching MB is explored within the LSP search values on the boundary of the step size {(±step size, 0), (0, ±step size), (0,0)} 7: Set vectors {(MVA), (MVL)}, as illustrated in Figure 2. 8: END IFELSE |
Step 3 | Matching MB is then explored within the LSP search values on the boundary of the step size {(±step size, 0), (0, ±step size), (0,0)} and the set vectors {(MVA), (MVL)}, as illustrated in Figure 2 The PDE algorithm is used to stop the partial sum matching distortion calculation between the current macroblock and candidate macroblock as soon as the matching distortion exceeds the current minimum distortion, resulting in the remaining computations to be avoided, hence, speeding up the search. |
Step 4 | Let Er represent the error of the matching MB in step 3. IF Er < Th: The process is terminated and the matching MB provides the motion vector. ELSE - Location of the matching MB in Step 3 is used as the center of the search window - SSP defined from the four points, i.e., {(±1, 0), (0, ±1)}, will be examined. End IFELSE IF matching MB stays in the center of the search window -Computation is completed ELSE - Go to Step 1 - The matching center provides the parameters of the motion vector. |
4. Simulation Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Sequence | Format | FS | PDE | MPBMLS |
---|---|---|---|---|
Claire | QCIF | 184.56 | 184.6 | 48.98 |
Akiyo | QCIF | 184.56 | 184.6 | 46.2 |
Carphone | QCIF | 184.56 | 184.6 | 170.2 |
News | CIF | 204.28 | 204.3 | 121.6 |
Stefan | CIF | 204.28 | 204.3 | 204.3 |
Coastguard | CIF | 204.28 | 204.3 | 204.3 |
Sequence | Format | FS | PDE | MPBMLS |
---|---|---|---|---|
Claire | QCIF | 0.351 | 0.18 | 0.06 |
Akiyo | QCIF | 0.334 | 0.11 | 0.01 |
Carphone | QCIF | 0.336 | 0.18 | 0.15 |
News | CIF | 1.492 | 0.65 | 0.38 |
Stefan | CIF | 1.464 | 1.09 | 0.88 |
Coastguard | CIF | 1.485 | 1.19 | 1.03 |
Sequence | Format | FS | PDE | MPBMLS |
---|---|---|---|---|
Claire | QCIF | 9.287 | 9.287 | 9.29 |
Akiyo | QCIF | 9.399 | 9.399 | 9.399 |
Carphone | QCIF | 56.44 | 56.44 | 56.44 |
News | CIF | 30.33 | 30.33 | 30.33 |
Stefan | CIF | 556.1 | 556.1 | 556.1 |
Coastguard | CIF | 158.3 | 158.3 | 158.3 |
Sequence | Format | FS | PDE | MPBMLS |
---|---|---|---|---|
Claire | QCIF | 38.94 | 38.94 | 38.94 |
Akiyo | QCIF | 39.61 | 39.61 | 39.61 |
Carphone | QCIF | 30.82 | 30.82 | 30.81 |
News | CIF | 33.48 | 33.48 | 33.47 |
Stefan | CIF | 22.16 | 22.16 | 22.16 |
Coastguard | CIF | 26.19 | 26.19 | 26.19 |
Sequence | FS | DS | NTSS | 4SS | SESTSS | ARPS | MPBMLY |
---|---|---|---|---|---|---|---|
Claire | 184.6 | 11.63 | 15.09 | 14.77 | 16.13 | 5.191 | 2.128 |
Akiyo | 184.6 | 11.46 | 14.76 | 14.67 | 16.2 | 4.958 | 1.938 |
Carphone | 184.6 | 13.76 | 17.71 | 16.12 | 15.73 | 7.74 | 7.06 |
News | 204.3 | 13.1 | 17.07 | 16.38 | 16.92 | 6.058 | 3.889 |
Stefan | 204.3 | 17.69 | 22.56 | 19.05 | 16.11 | 9.641 | 9.619 |
Coastguard | 204.3 | 19.08 | 27.26 | 19.91 | 16.52 | 9.474 | 8.952 |
Sequence | FS | DS | NTSS | 4SS | SESTSS | ARPS | MPBMLY |
---|---|---|---|---|---|---|---|
Claire | 0.351 | 0.037 | 0.031 | 0.031 | 0.037 | 0.025 | 0.015 |
Akiyo | 0.354 | 0.036 | 0.031 | 0.031 | 0.037 | 0.023 | 0.006 |
Carphone | 0.338 | 0.039 | 0.036 | 0.032 | 0.035 | 0.031 | 0.033 |
News | 1.539 | 0.161 | 0.142 | 0.136 | 0.151 | 0.112 | 0.079 |
Stefan | 1.537 | 0.267 | 0.232 | 0.174 | 0.15 | 0.158 | 0.139 |
Coastguard | 1.551 | 0.263 | 0.235 | 0.178 | 0.15 | 0.152 | 0.14 |
Sequence | FS | DS | NTSS | 4SS | SESTSS | ARPS | MPBMLY |
---|---|---|---|---|---|---|---|
Claire | 9.287 | 9.287 | 9.287 | 9.355 | 9.458 | 9.289 | 9.292 |
Akiyo | 9.399 | 9.399 | 9.399 | 9.399 | 9.408 | 9.399 | 9.399 |
Carphone | 56.44 | 58.16 | 57.56 | 62.12 | 69.62 | 60.02 | 59.08 |
News | 27.29 | 29.41 | 28.2 | 29.6 | 31.22 | 29.81 | 28.64 |
Stefan | 556.1 | 661.4 | 607.2 | 651.5 | 714.5 | 608 | 594 |
Coastguard | 158.3 | 167.4 | 164.3 | 166 | 182.5 | 164.1 | 161.6 |
Sequence | FS | DS | NTSS | 4SS | SESTSS | ARPS | MPBMLY |
---|---|---|---|---|---|---|---|
Claire | 38.94 | 38.94 | 38.94 | 38.92 | 38.89 | 38.94 | 38.94 |
Akiyo | 39.61 | 39.61 | 39.61 | 39.61 | 39.61 | 39.61 | 39.61 |
Carphone | 30.82 | 30.69 | 30.7 | 30.4 | 30.1 | 30.58 | 30.6 |
News | 33.77 | 33.45 | 33.63 | 33.42 | 33.19 | 33.39 | 33.56 |
Stefan | 22.16 | 21.49 | 21.81 | 21.51 | 21.04 | 21.82 | 21.93 |
Coastguard | 26.19 | 25.98 | 26.05 | 26.02 | 25.6 | 26.05 | 26.11 |
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Ahmed, Z.; Hussain, A.J.; Khan, W.; Baker, T.; Al-Askar, H.; Lunn, J.; Al-Shabandar, R.; Al-Jumeily, D.; Liatsis, P. Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics. Remote Sens. 2020, 12, 1004. https://doi.org/10.3390/rs12061004
Ahmed Z, Hussain AJ, Khan W, Baker T, Al-Askar H, Lunn J, Al-Shabandar R, Al-Jumeily D, Liatsis P. Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics. Remote Sensing. 2020; 12(6):1004. https://doi.org/10.3390/rs12061004
Chicago/Turabian StyleAhmed, Zayneb, Abir Jaafar Hussain, Wasiq Khan, Thar Baker, Haya Al-Askar, Janet Lunn, Raghad Al-Shabandar, Dhiya Al-Jumeily, and Panos Liatsis. 2020. "Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics" Remote Sensing 12, no. 6: 1004. https://doi.org/10.3390/rs12061004