A Novel Motion Estimation Method Based on Normalized Cross Correlation for Video Compression View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008-01-01

AUTHORS

Shou-Der Wei , Wei-Hau Pan , Shang-Hong Lai

ABSTRACT

In this paper we propose to use the normalized cross correlation (NCC) as the similarity measure for block-based motion estimation (ME) to replace the sum of absolute difference (SAD) measure. NCC is a more suitable similarity measure than SAD for reducing the temporal redundancy in video comparison since we can obtain flatter residual after motion compensation by using the NCC as the similarity measure in the motion estimation. The flat residual results in large DC term and smaller AC term, which means less information is lost after quantization. Thus, we can obtain better quality in the compressed video. Experimental results show the proposed NCC-based motion estimation algorithm can provide similar PSNR but better SSIM than the traditional full search ME with the SAD measure. More... »

PAGES

338-347

Book

TITLE

Advances in Multimedia Modeling

ISBN

978-3-540-77407-5
978-3-540-77409-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-77409-9_32

DOI

http://dx.doi.org/10.1007/978-3-540-77409-9_32

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003615624


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wei", 
        "givenName": "Shou-Der", 
        "id": "sg:person.013224413773.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013224413773.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pan", 
        "givenName": "Wei-Hau", 
        "id": "sg:person.010476750713.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010476750713.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lai", 
        "givenName": "Shang-Hong", 
        "id": "sg:person.010301330015.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010301330015.11"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2008-01-01", 
    "datePublishedReg": "2008-01-01", 
    "description": "In this paper we propose to use the normalized cross correlation (NCC) as the similarity measure for block-based motion estimation (ME) to replace the sum of absolute difference (SAD) measure. NCC is a more suitable similarity measure than SAD for reducing the temporal redundancy in video comparison since we can obtain flatter residual after motion compensation by using the NCC as the similarity measure in the motion estimation. The flat residual results in large DC term and smaller AC term, which means less information is lost after quantization. Thus, we can obtain better quality in the compressed video. Experimental results show the proposed NCC-based motion estimation algorithm can provide similar PSNR but better SSIM than the traditional full search ME with the SAD measure.", 
    "editor": [
      {
        "familyName": "Satoh", 
        "givenName": "Shin\u2019ichi", 
        "type": "Person"
      }, 
      {
        "familyName": "Nack", 
        "givenName": "Frank", 
        "type": "Person"
      }, 
      {
        "familyName": "Etoh", 
        "givenName": "Minoru", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-77409-9_32", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-77407-5", 
        "978-3-540-77409-9"
      ], 
      "name": "Advances in Multimedia Modeling", 
      "type": "Book"
    }, 
    "keywords": [
      "normalized cross correlation", 
      "motion estimation", 
      "similarity measure", 
      "block-based motion estimation", 
      "full search motion estimation", 
      "motion estimation algorithm", 
      "motion estimation method", 
      "absolute difference measures", 
      "novel motion estimation method", 
      "suitable similarity measure", 
      "video compression", 
      "temporal redundancy", 
      "similar PSNR", 
      "better SSIM", 
      "video comparison", 
      "motion compensation", 
      "cross correlation", 
      "estimation algorithm", 
      "AC term", 
      "SAD measures", 
      "experimental results", 
      "dc term", 
      "estimation method", 
      "PSNR", 
      "SSIM", 
      "video", 
      "good quality", 
      "algorithm", 
      "redundancy", 
      "estimation", 
      "residual results", 
      "difference measures", 
      "quantization", 
      "less information", 
      "information", 
      "compression", 
      "terms", 
      "SAD", 
      "quality", 
      "method", 
      "results", 
      "measures", 
      "compensation", 
      "sum", 
      "comparison", 
      "correlation", 
      "paper", 
      "flat residual results", 
      "large DC term", 
      "smaller AC term", 
      "NCC-based motion estimation algorithm", 
      "traditional full search ME", 
      "search ME"
    ], 
    "name": "A Novel Motion Estimation Method Based on Normalized Cross Correlation for Video Compression", 
    "pagination": "338-347", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1003615624"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-77409-9_32"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-77409-9_32", 
      "https://app.dimensions.ai/details/publication/pub.1003615624"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-12-01T20:11", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/chapter/chapter_454.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-77409-9_32"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-77409-9_32'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-77409-9_32'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-77409-9_32'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-77409-9_32'


 

This table displays all metadata directly associated to this object as RDF triples.

137 TRIPLES      23 PREDICATES      78 URIs      71 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-77409-9_32 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N25dd1b6c46d841db8f6f975a1149355a
4 schema:datePublished 2008-01-01
5 schema:datePublishedReg 2008-01-01
6 schema:description In this paper we propose to use the normalized cross correlation (NCC) as the similarity measure for block-based motion estimation (ME) to replace the sum of absolute difference (SAD) measure. NCC is a more suitable similarity measure than SAD for reducing the temporal redundancy in video comparison since we can obtain flatter residual after motion compensation by using the NCC as the similarity measure in the motion estimation. The flat residual results in large DC term and smaller AC term, which means less information is lost after quantization. Thus, we can obtain better quality in the compressed video. Experimental results show the proposed NCC-based motion estimation algorithm can provide similar PSNR but better SSIM than the traditional full search ME with the SAD measure.
7 schema:editor N7718c37fdb3546f796933b81ea95b160
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N8b8aee039bd4497da6e526b01893151e
12 schema:keywords AC term
13 NCC-based motion estimation algorithm
14 PSNR
15 SAD
16 SAD measures
17 SSIM
18 absolute difference measures
19 algorithm
20 better SSIM
21 block-based motion estimation
22 comparison
23 compensation
24 compression
25 correlation
26 cross correlation
27 dc term
28 difference measures
29 estimation
30 estimation algorithm
31 estimation method
32 experimental results
33 flat residual results
34 full search motion estimation
35 good quality
36 information
37 large DC term
38 less information
39 measures
40 method
41 motion compensation
42 motion estimation
43 motion estimation algorithm
44 motion estimation method
45 normalized cross correlation
46 novel motion estimation method
47 paper
48 quality
49 quantization
50 redundancy
51 residual results
52 results
53 search ME
54 similar PSNR
55 similarity measure
56 smaller AC term
57 suitable similarity measure
58 sum
59 temporal redundancy
60 terms
61 traditional full search ME
62 video
63 video comparison
64 video compression
65 schema:name A Novel Motion Estimation Method Based on Normalized Cross Correlation for Video Compression
66 schema:pagination 338-347
67 schema:productId N9c85cd653cc44b4eab2816e180635938
68 Nb2a690577f82438d8a002589bb599f0d
69 schema:publisher N4bf34be0489c43d2a2f1eec2f027fb25
70 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003615624
71 https://doi.org/10.1007/978-3-540-77409-9_32
72 schema:sdDatePublished 2021-12-01T20:11
73 schema:sdLicense https://scigraph.springernature.com/explorer/license/
74 schema:sdPublisher N62b68986ac5f42d793425d8c7b4dc8c2
75 schema:url https://doi.org/10.1007/978-3-540-77409-9_32
76 sgo:license sg:explorer/license/
77 sgo:sdDataset chapters
78 rdf:type schema:Chapter
79 N1c16154dd20649aba0b78dbd02b57d34 rdf:first N5ec148f5afcb41b38fe664b477e43d89
80 rdf:rest N87887c8c14a2441cb1760b14c0a746bf
81 N25dd1b6c46d841db8f6f975a1149355a rdf:first sg:person.013224413773.06
82 rdf:rest Nf00af1e71ef6454e8c94db8047c2d5b0
83 N42f456789b0843abb607a7993a2d238c schema:familyName Satoh
84 schema:givenName Shin’ichi
85 rdf:type schema:Person
86 N4bf34be0489c43d2a2f1eec2f027fb25 schema:name Springer Nature
87 rdf:type schema:Organisation
88 N5ec148f5afcb41b38fe664b477e43d89 schema:familyName Nack
89 schema:givenName Frank
90 rdf:type schema:Person
91 N62b68986ac5f42d793425d8c7b4dc8c2 schema:name Springer Nature - SN SciGraph project
92 rdf:type schema:Organization
93 N7718c37fdb3546f796933b81ea95b160 rdf:first N42f456789b0843abb607a7993a2d238c
94 rdf:rest N1c16154dd20649aba0b78dbd02b57d34
95 N84cc6110f6b94cfe98f21b405c3afd3e rdf:first sg:person.010301330015.11
96 rdf:rest rdf:nil
97 N87887c8c14a2441cb1760b14c0a746bf rdf:first N956d32b786da48e6a6a6413157f6530c
98 rdf:rest rdf:nil
99 N8b8aee039bd4497da6e526b01893151e schema:isbn 978-3-540-77407-5
100 978-3-540-77409-9
101 schema:name Advances in Multimedia Modeling
102 rdf:type schema:Book
103 N956d32b786da48e6a6a6413157f6530c schema:familyName Etoh
104 schema:givenName Minoru
105 rdf:type schema:Person
106 N9c85cd653cc44b4eab2816e180635938 schema:name dimensions_id
107 schema:value pub.1003615624
108 rdf:type schema:PropertyValue
109 Nb2a690577f82438d8a002589bb599f0d schema:name doi
110 schema:value 10.1007/978-3-540-77409-9_32
111 rdf:type schema:PropertyValue
112 Nf00af1e71ef6454e8c94db8047c2d5b0 rdf:first sg:person.010476750713.22
113 rdf:rest N84cc6110f6b94cfe98f21b405c3afd3e
114 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
115 schema:name Information and Computing Sciences
116 rdf:type schema:DefinedTerm
117 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
118 schema:name Artificial Intelligence and Image Processing
119 rdf:type schema:DefinedTerm
120 sg:person.010301330015.11 schema:affiliation grid-institutes:grid.38348.34
121 schema:familyName Lai
122 schema:givenName Shang-Hong
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010301330015.11
124 rdf:type schema:Person
125 sg:person.010476750713.22 schema:affiliation grid-institutes:grid.38348.34
126 schema:familyName Pan
127 schema:givenName Wei-Hau
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010476750713.22
129 rdf:type schema:Person
130 sg:person.013224413773.06 schema:affiliation grid-institutes:grid.38348.34
131 schema:familyName Wei
132 schema:givenName Shou-Der
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013224413773.06
134 rdf:type schema:Person
135 grid-institutes:grid.38348.34 schema:alternateName Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
136 schema:name Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
137 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...