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Compressive sampling for digital video signal compression involving dynamic sparsity
Wahidah I.a,b, Suksmono A.B.b, Hendrawanb, Mengko T.L.R.b
a Faculty of Electrical and Communications Engineering, Institut Teknologi Telkom, 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]In this work, we consider a compressive sampling problem implemented in digital video compression. The compressive sensing method has a tremendous growth in recent researches. Inevitably, video compression field should explore alternatives in this novel approach to overcome some signal reconstruction drawbacks. We present the video signal representation and measurement based on dynamic sparsity of the source. Video sequences having high spatial and temporal redundancy are shown to take the most advantage out of compressive sampling technique. Firstly, we model the signal in a sparse domain using classic transforms such as DCT or wavelet. Then, in projection stage, scene dynamic is measured by the number of non significant coefficients. We use a quite small number C as threshold of these coefficients. In one group of picture (GOP), each frame is tested whether classified as sparse frame, hence undergoing compressive sampling; otherwise it would be processed by conventional sampling. The additional consideration of dynamic sparsity is expected to improve reconstruction quality, especially for video sequence with low redundancy property and low measurement rate. Furthermore, we compare the CVS results with those of MPEG-4 in terms of PSNR and compression ratio. © 2012 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]Compressive sampling,Compressive sensing,Digital video compression,Digital video signals,Group of pictures,Measurement-based,ON dynamics,Reconstruction quality,Temporal redundancy,Video sequences,Video signal[/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]compressive sensing,compressive video sampling,dynamic sparsity,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/TSSA.2012.6366019[/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]