An edge aware motion modeling technique leveraging on the discrete cosine basis oriented motion model and frame super resolution

Ashek Ahmmed, Manoranjan Paul, Mark Pickering, Andrew Lambert

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)


To capture motion homogeneity between successive frames, the edge position difference (EPD) measure based motion modeling (EPD-MM) has shown good motion compensation capabilities. The EPD-MM technique is underpinned by the fact that from one frame to next, edges map to edges and such mapping can be captured by an appropriate motion model. An example of such a motion model is the discrete cosine basis oriented (DCO) motion model, which can capture complex motion and has a smooth and sparse representation. However, for higher resolution video sequences, the baseline EPD-MM approach equipped with the DCO motion model, may fail to approximate the underlying motion field accurately. This is due to the difficulty in fitting motion model parameters by incorporating significantly large number of moving edge pixels. Observing the fact that in lower resolution version of the current frame C, the same scene structure is present although scaled down moving objects contain smaller number of edge pixels; in this paper we propose to carry out the EPD-MM technique, augmented by the DCO motion model, over lower resolution form of C. The resultant edge motion compensated prediction is then upsampled back to the original resolution of C, employing single image super resolution (SISR) technique. Experimental results show an improved prediction PSNR of 1.85 dB, on average, from the proposed approach compared to that of the baseline EPD-MM. Moreover, if this predicted frame is employed as an additional reference frame to encode C, bit rate savings of up to 7.90% is achievable over a HEVC reference.

Original languageEnglish
Title of host publicationProceedings - DCC 2022
Subtitle of host publication2022 Data Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781665478939
ISBN (Print)9781665478946 (Print on demand)
Publication statusE-pub ahead of print - 04 Jul 2022
Event2022 Data Compression Conference: DCC 2022 - Snowbird, United States
Duration: 22 Mar 202225 Mar 2022 (Conference website) (Conference proceedings)

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Conference2022 Data Compression Conference
Country/TerritoryUnited States
OtherThe Data Compression Conference (DCC) is an international forum for current work on data compression and related applications.
Internet address


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