Sparse signal modeling in video coding:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Düren
Shaker Verlag
2022
|
| Edition: | 1. Auflage |
| Series: | Aachen Series on Multimedia and Communications Engineering
Volume 24 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xv, 127 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 212 g |
| ISBN: | 9783844084016 3844084010 |
Staff View
MARC
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| 100 | 1 | |a Schneider, Jens |d 1990- |e Verfasser |0 (DE-588)1251816657 |4 aut | |
| 245 | 1 | 0 | |a Sparse signal modeling in video coding |c Jens Schneider |
| 250 | |a 1. Auflage | ||
| 264 | 1 | |a Düren |b Shaker Verlag |c 2022 | |
| 300 | |a xv, 127 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 212 g | ||
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| 502 | |b Dissertation |c RWTH Aachen University |d 2022 | ||
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| 650 | 0 | 7 | |a Spärliche Codierung |0 (DE-588)4610668-6 |2 gnd |9 rswk-swf |
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| 653 | |a Communications Engeneering | ||
| 653 | |a Multimedia | ||
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Record in the Search Index
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| adam_text | CONTENTS
1
INTRODUCTION
1
1.1
CONTRIBUTIONS
AND
STRUCTURE
OF
THIS
THESIS
........................................................
2
2
FUNDAMENTALS
5
2.1
MATHEMATICS
.......................................................................................................
5
2.1.1
KRONECKER
PRODUCT
AND
VECTORIZATION
.....................................................
5
2.1.2
VECTOR
AND
MATRIX
NORMS
.......................................................................
6
2.1.3
MOORE-PENROSE
PSEUDO
INVERSE
AND
LEAST
SQUARES
SOLUTION
................
7
2.2
VIDEO
CODING
FUNDAMENTALS
.............................................................................
7
2.2.1
THE
HYBRID
VIDEO
CODING
SCHEME
.......................................................
8
2.2.2
INTRA
PREDICTION
.......................................................................................
12
2.2.3
LOOP
FILTERING
..........................................................................................
14
2.2.4
PERFORMANCE
EVALUATION
..........................................................................
16
2.3 SPARSE
CODING
AND
DICTIONARY
LEARNING
..........................................................
18
2.3.1
MATHEMATICAL
FORMULATION
.....................................................................
18
2.3.2
ALGORITHMS
...............................................................................................
20
2.4
SPARSE
CODING-BASED
IMAGE
PROCESSING
.............................................................
24
2.4.1
BASIC
DICTIONARIES
-
CHOOSING
AND
LEARNING
.......................................
25
2.4.2
SPARSITY
IN
OVERCOMPLETE
DICTIONARIES
..................................................
29
3
SPARSE
CODING-BASED
APPROACHES
TO
INVERSE
PROBLEMS
31
3.1
DENOISING
............................................................................................................
32
3.1.1
SPARSE
CODING-BASED
DENOISING
.............................................................
33
3.1.2
ANALYSIS
..................................................................................................
35
3.1.3
SUMMARY
...............................................................................................
38
3.2
SUPER-RESOLUTION
...............................................................................................
38
3.2.1
DOWNSAMPLING
AND
UPSAMPLING
..........................................................
38
3.2.2
PERFORMANCE
EVALUATION
..........................................................................
40
3.2.3
DICTIONARY
LEARNING-BASED
SUPER-RESOLUTION
.......................................
44
3.2.4
DLSR
-
THE
CONVOLUTIONAL
POINT
OF
VIEW
...............................................
48
3.2.5
SUMMARY
...............................................................................................
52
3.3
COMPLEXITY
CONSIDERATIONS
...............................................................................
52
3.3.1
SPARSE
CODING
COMPLEXITY
.....................................................................
53
3.3.2
COMPLEXITY
REDUCTION
BY
SUB-DICTIONARIES
..........................................
56
3.3.3
SUMMARY
...............................................................................................
59
4
SPARSE
CODING-BASED
IN-LOOP
FILTERING
61
4.1
SPARSE
CODING-BASED
LOOP
FILTERING
IN
HEVC
...................................................
63
4.2
SPARSE
CODING-BASED
LOOP
FILTERING
IN
WC
......................................................
64
4.3
SUMMARY
............................................................................................................
69
CONTENTS
5
VIDEO
CODING
WITH
VARYING RESOLUTION
71
5.1
SCALABLE
VIDEO
CODING
........................................................................................
71
5.1.1
DLSR
ON
CODED
BASE
LAYER
PICTURES
........................................................
73
5.1.2
SPARSE
CODING-BASED
INTER-LAYER
PREDICTION
IN
SHVC
...........................
75
5.2 REFERENCE
PICTURE
RESAMPLING
(RPR)
................................................................
76
5.2.1
TEST
CONDITIONS
AND
PERFORMANCE
EVALUATION
........................................
77
5.2.2
DICTIONARY
LEARNING-BASED
RPR
IN
WC
...................................................
79
5.2.3
EXPERIMENTAL
SETUP
AND
RESULTS
.............................................................
80
5.3
SPARSE
CODING-BASED
ADAPTIVE
RESOLUTION
CHANGE
...........................................
82
5.3.1
ADAPTIVE
RESOLUTION
CHANGE
USING
UNCODED
SUBPICTURES
...................
82
5.3.2
SIMULATION
SETUP
AND
RESULTS
................................................................
85
5.3.3
SUMMARY
................................................................................................
87
5.4
SUMMARY
AND
CONCLUSION
...................................................................................
87
6
SPARSE
CODING-BASED
INTRA
PREDICTION
89
6.1
SCIP
-
THE
CORE
IDEA
...........................................................................................
89
6.1.1
DICTIONARY
AND
REGRESSOR
LEARNING
........................................................
91
6.2
EXPERIMENTAL
SETUP
AND
RESULTS
........................................................................
93
6.2.1
INITIAL
ASSESSMENT
...................................................................................
93
6.2.2
WC
INTEGRATION
AND
RATE-DISTORTION
PERFORMANCE
.............................
96
6.3
SUMMARY
.............................................................................................................
97
7
SUMMARY
AND
OUTLOOK
101
7.1
SUMMARY
................................................................................................................
101
7.2
OUTLOOK
...................................................................................................................
102
A
DATA
SETS
107
A.L
TRAINING
SET
5^,!
AND
TEST
SET
5^4
........................................................................
107
A.2
JVET
TEST
SEQUENCES
.............................................................................................
107
A.3
SHVC
TEST
SEQUENCES
.............................................................................................
108
A.
4
TRAINING
SET
USED
FOR
SCIP
.....................................................................................
108
B
AUXILIARY
RESULTS 113
B.
L
DENOISING
BY
CLASSICAL
WIENER
FILTER
.....................................................................
113
B.2
CORRELATION
BETWEEN
SIGNAL
AND
NOISE
...................................................................
113
B.3
ORTHOGONALITY
OF
ERROR
TERMS
................................................................................
114
B.4
PRELIMINARY
EXPERIMENT
REGARDING
SCLF
..........................................................
115
BIBLIOGRAPHY
117
|
| adam_txt |
CONTENTS
1
INTRODUCTION
1
1.1
CONTRIBUTIONS
AND
STRUCTURE
OF
THIS
THESIS
.
2
2
FUNDAMENTALS
5
2.1
MATHEMATICS
.
5
2.1.1
KRONECKER
PRODUCT
AND
VECTORIZATION
.
5
2.1.2
VECTOR
AND
MATRIX
NORMS
.
6
2.1.3
MOORE-PENROSE
PSEUDO
INVERSE
AND
LEAST
SQUARES
SOLUTION
.
7
2.2
VIDEO
CODING
FUNDAMENTALS
.
7
2.2.1
THE
HYBRID
VIDEO
CODING
SCHEME
.
8
2.2.2
INTRA
PREDICTION
.
12
2.2.3
LOOP
FILTERING
.
14
2.2.4
PERFORMANCE
EVALUATION
.
16
2.3 SPARSE
CODING
AND
DICTIONARY
LEARNING
.
18
2.3.1
MATHEMATICAL
FORMULATION
.
18
2.3.2
ALGORITHMS
.
20
2.4
SPARSE
CODING-BASED
IMAGE
PROCESSING
.
24
2.4.1
BASIC
DICTIONARIES
-
CHOOSING
AND
LEARNING
.
25
2.4.2
SPARSITY
IN
OVERCOMPLETE
DICTIONARIES
.
29
3
SPARSE
CODING-BASED
APPROACHES
TO
INVERSE
PROBLEMS
31
3.1
DENOISING
.
32
3.1.1
SPARSE
CODING-BASED
DENOISING
.
33
3.1.2
ANALYSIS
.
35
3.1.3
SUMMARY
.
38
3.2
SUPER-RESOLUTION
.
38
3.2.1
DOWNSAMPLING
AND
UPSAMPLING
.
38
3.2.2
PERFORMANCE
EVALUATION
.
40
3.2.3
DICTIONARY
LEARNING-BASED
SUPER-RESOLUTION
.
44
3.2.4
DLSR
-
THE
CONVOLUTIONAL
POINT
OF
VIEW
.
48
3.2.5
SUMMARY
.
52
3.3
COMPLEXITY
CONSIDERATIONS
.
52
3.3.1
SPARSE
CODING
COMPLEXITY
.
53
3.3.2
COMPLEXITY
REDUCTION
BY
SUB-DICTIONARIES
.
56
3.3.3
SUMMARY
.
59
4
SPARSE
CODING-BASED
IN-LOOP
FILTERING
61
4.1
SPARSE
CODING-BASED
LOOP
FILTERING
IN
HEVC
.
63
4.2
SPARSE
CODING-BASED
LOOP
FILTERING
IN
WC
.
64
4.3
SUMMARY
.
69
CONTENTS
5
VIDEO
CODING
WITH
VARYING RESOLUTION
71
5.1
SCALABLE
VIDEO
CODING
.
71
5.1.1
DLSR
ON
CODED
BASE
LAYER
PICTURES
.
73
5.1.2
SPARSE
CODING-BASED
INTER-LAYER
PREDICTION
IN
SHVC
.
75
5.2 REFERENCE
PICTURE
RESAMPLING
(RPR)
.
76
5.2.1
TEST
CONDITIONS
AND
PERFORMANCE
EVALUATION
.
77
5.2.2
DICTIONARY
LEARNING-BASED
RPR
IN
WC
.
79
5.2.3
EXPERIMENTAL
SETUP
AND
RESULTS
.
80
5.3
SPARSE
CODING-BASED
ADAPTIVE
RESOLUTION
CHANGE
.
82
5.3.1
ADAPTIVE
RESOLUTION
CHANGE
USING
UNCODED
SUBPICTURES
.
82
5.3.2
SIMULATION
SETUP
AND
RESULTS
.
85
5.3.3
SUMMARY
.
87
5.4
SUMMARY
AND
CONCLUSION
.
87
6
SPARSE
CODING-BASED
INTRA
PREDICTION
89
6.1
SCIP
-
THE
CORE
IDEA
.
89
6.1.1
DICTIONARY
AND
REGRESSOR
LEARNING
.
91
6.2
EXPERIMENTAL
SETUP
AND
RESULTS
.
93
6.2.1
INITIAL
ASSESSMENT
.
93
6.2.2
WC
INTEGRATION
AND
RATE-DISTORTION
PERFORMANCE
.
96
6.3
SUMMARY
.
97
7
SUMMARY
AND
OUTLOOK
101
7.1
SUMMARY
.
101
7.2
OUTLOOK
.
102
A
DATA
SETS
107
A.L
TRAINING
SET
5^,!
AND
TEST
SET
5^4
.
107
A.2
JVET
TEST
SEQUENCES
.
107
A.3
SHVC
TEST
SEQUENCES
.
108
A.
4
TRAINING
SET
USED
FOR
SCIP
.
108
B
AUXILIARY
RESULTS 113
B.
L
DENOISING
BY
CLASSICAL
WIENER
FILTER
.
113
B.2
CORRELATION
BETWEEN
SIGNAL
AND
NOISE
.
113
B.3
ORTHOGONALITY
OF
ERROR
TERMS
.
114
B.4
PRELIMINARY
EXPERIMENT
REGARDING
SCLF
.
115
BIBLIOGRAPHY
117 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Schneider, Jens 1990- |
| author_GND | (DE-588)1251816657 |
| author_facet | Schneider, Jens 1990- |
| author_role | aut |
| author_sort | Schneider, Jens 1990- |
| author_variant | j s js |
| building | Verbundindex |
| bvnumber | BV047858605 |
| classification_rvk | ST 325 |
| ctrlnum | (OCoLC)1310250321 (DE-599)DNB1248146050 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1. Auflage |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV047858605 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:17:06Z |
| indexdate | 2024-07-10T09:23:17Z |
| institution | BVB |
| institution_GND | (DE-588)1064118135 |
| isbn | 9783844084016 3844084010 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033241277 |
| oclc_num | 1310250321 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | xv, 127 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 212 g |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Shaker Verlag |
| record_format | marc |
| series | Aachen Series on Multimedia and Communications Engineering |
| series2 | Aachen Series on Multimedia and Communications Engineering |
| spelling | Schneider, Jens 1990- Verfasser (DE-588)1251816657 aut Sparse signal modeling in video coding Jens Schneider 1. Auflage Düren Shaker Verlag 2022 xv, 127 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 212 g txt rdacontent n rdamedia nc rdacarrier Aachen Series on Multimedia and Communications Engineering Volume 24 Dissertation RWTH Aachen University 2022 Video (DE-588)4078895-7 gnd rswk-swf Spärliche Codierung (DE-588)4610668-6 gnd rswk-swf Videocoding Communications Engeneering Multimedia (DE-588)4113937-9 Hochschulschrift gnd-content Video (DE-588)4078895-7 s Spärliche Codierung (DE-588)4610668-6 s DE-604 Shaker Verlag (DE-588)1064118135 pbl Aachen Series on Multimedia and Communications Engineering Volume 24 (DE-604)BV019760223 24 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033241277&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20211221 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Schneider, Jens 1990- Sparse signal modeling in video coding Aachen Series on Multimedia and Communications Engineering Video (DE-588)4078895-7 gnd Spärliche Codierung (DE-588)4610668-6 gnd |
| subject_GND | (DE-588)4078895-7 (DE-588)4610668-6 (DE-588)4113937-9 |
| title | Sparse signal modeling in video coding |
| title_auth | Sparse signal modeling in video coding |
| title_exact_search | Sparse signal modeling in video coding |
| title_exact_search_txtP | Sparse signal modeling in video coding |
| title_full | Sparse signal modeling in video coding Jens Schneider |
| title_fullStr | Sparse signal modeling in video coding Jens Schneider |
| title_full_unstemmed | Sparse signal modeling in video coding Jens Schneider |
| title_short | Sparse signal modeling in video coding |
| title_sort | sparse signal modeling in video coding |
| topic | Video (DE-588)4078895-7 gnd Spärliche Codierung (DE-588)4610668-6 gnd |
| topic_facet | Video Spärliche Codierung Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033241277&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV019760223 |
| work_keys_str_mv | AT schneiderjens sparsesignalmodelinginvideocoding AT shakerverlag sparsesignalmodelinginvideocoding |