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Correcting temporal artifacts in compressive video sampling with motion estimation
Wahidah I.a, Hendrawanb, Suksmono A.B.b, Mengko T.L.R.b
a Faculty of Electrical and Communications Engineering, Telkom University, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]We present an algorithm for finding solutions to combat artifacts due to temporal coding of video sequence. The compressive sensing method has been utilized tremendously in recent years to solve underdetermined problems. The use of compressive sampling in video coding is promising since video signal has a sparse property and its volume is massive. In order to reduce spatial and temporal redundancy, we apply DCT and some motion estimation algorithms. Then we integrate those methods with the projection transformation based on compressive sampling principles. Despite the large amount of data volume in a video, we can still meet the requirement on sparsity in the residual frames resulted from motion estimation and compensation process. The basic motion estimation, i.e. The differential method, cannot handle the temporal artifacts reliably, especially for video sequences with fast motion objects. However, we have to consider the constraints of computational complexity at the encoder side. In this research, we compare the reconstruction results of various block matching algorithms, such as differential method, three step search, new TSS, and the simple and efficient search. © 2013 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Block Matching,Block matching algorithms,Compressive sampling,Motion estimation algorithm,Motion estimation and compensations,Projection transformation,Sparse representation,Spatial and temporal redundancies[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]block matching,Compressive sampling,motion estimation,sparse representation,video coding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/APCC.2013.6766038[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]