Deinterlacing Using Hierarchical Motion Analysis

Abstract

Abstract-A motion-compensated deinterlacing scheme based on hierarchical motion analysis is presented. According to deinterlacing steps, our contribution can be divided into four parts: motion estimation, motion state analysis, motion consistency analysis, and finer-grained interpolation. In motion estimation, we introduce a Gaussian noise model for choosing the best motion vector for each block, and make a tradeoff between utilizing previous de-interlaced frames and avoiding error propagation. A directional interpolation method is also introduced in this part for backward fields. In motion state analysis, we define two motion states for each pixel, thus achieve a compromise between traditional block-based strategies and the extreme pixel-based case. In motion consistency analysis, we propose to measure both the motion vector consistency and the motion state consistency in order to determine whether the previous two parts should be performed again with a different block size. In finer-grained interpolation, we utilize a combination of recursive median filters to generate the final results. Experimental results show that all of the proposed techniques are effective, either objectively or subjectively. As a result, we can achieve much higher image quality, with an average gain of about 1.83 dB in terms of peak signal-to-noise ratio. Moreover, the increased computation complexity is marginal.

Spatiotemporal deinterlacing using a maximuma posterioriestimator based on multiple-field registration

“…1 We can categorize the existing algorithms into three groups as follows: spatial deinterlacing, [2][3][4][5][6][7][8] motion-adaptive (MA) deinterlacing, [9][10][11] and motion compensation (MC)-based deinterlacing. [12][13][14][15][16][17][18][19][20][21][22] Spatial deinterlacing algorithms have the advantages of simple operation and straightforward integration into hardware. The edge-based line averaging (ELA) algorithm is one of the most popular deinterlacing algorithms in this category.…”

“…[13][14][15][16][17][18][19][20][21][22] For example, Chang et al presented an adaptive four-field global/local motion-compensated approach where the same parity four-field motion detection and four-field motion estimation detect static areas and fast motion by four reference fields, and global motion estimation detects camera panning and zooming motions. 13 However, this algorithm may give rise to feathering artifacts when the assumption of strong continuity of local motion does not work.…”

Video resampling algorithm for simultaneous deinterlacing and image upscaling with reduced jagged edge artifacts

“…These methods can be roughly classified into linear filtering interpolation (LFI) and edge directional interpolation (EDI). Among the deinterlacing methods, LFI approaches [2][3][4][5][6][7] can be categorized into spatial (intra-field), temporal (inter-field), and spatiotemporal methods, according to the field information. In order to obtain progressive images, missing pixels have to be reconstructed using linear filters according to the spatial correlations, the temporal correlations, and both the spatial and temporal correlations in interlaced video sequences.…”

“…In order to obtain progressive images, missing pixels have to be reconstructed using linear filters according to the spatial correlations, the temporal correlations, and both the spatial and temporal correlations in interlaced video sequences. Particularly, some algorithms [5][6][7] discover missing pixel values by interpolating the pixels along motion trajectories, because temporal correlation is dependent on motion information. Among the image upscaling methods, LFI approaches [8][9][10][11][12] design a particular interpolation kernel, which can be applied to the http://asp.eurasipjournals.com/content/2013/1/188 entire image.…”

Spatio-temporal de-interlacing based on maximum likelihood estimation

“…For several decades, a lot of de-interlacing algorithms have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13]. We can categorize the existing algorithms into three groups as follows: spatial de-interlacing [1][2][3][4][5][6], temporal deinterlacing [7][8], and spatio-temporal de-interlacing algorithms [9][10][11][12][13].…”