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De Interlacing Techniques
 

De Interlacing Techniques

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This presentation discusses the various techniques used for video de-interlacing.

This presentation discusses the various techniques used for video de-interlacing.

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    De Interlacing Techniques De Interlacing Techniques Presentation Transcript

    • De-Interlacing Techniques
      Ramesh Prasad
    • Introduction
      De-interlacing is the process of converting interlaced video, such as common analog television signals or 1080i format HDTV signals, into a non-interlaced form.
      Interlaced video frame consists of two sub-fields taken in sequence, each sequentially scanned at odd and even lines of the image sensor; analog television employed this technique because it allowed for less transmission bandwidth and matched the properties of CRT screens.
      However, most current displays are inherently progressive, so the two fields need to be combined to a single frame, which leads to various visual defects which the de-interlacing process should try to avoid.
    • Introduction
      Progressive Frame
      Odd Field
      Even Field
    • Introduction
      Interlacing is a form of “Spatio-temporal” sub sampling
      De-interlacing is the reverse operation, aiming at removal of sub-sampling artifacts
      For stationary pictures (no camera or object motion or intensity changes) the de-interlacing is trivial as alternating even and odd fields completely describe the captured scene
      However, for pictures with camera/object motion and intensity changes (which always is the case with real life scenario); de-interlacing becomes non-trivial and entails processing, as it requires interpolation of picture data that was never transmitted or even captured.
      A good de-interlacing algorithm should try to avoid interlacing artifacts as much as possible and not sacrifice image quality in the process.
    • Introduction
    • Techniques for De-Interlacing
      Intra field de-interlacing
      Inter field de-interlacing
      Spatio-Temporal Interpolation
      Motion adaptive de-interlacing
      Motion compensated de-interlacing
    • Intra field de-interlacing - Overview
      Based on Spatial filtering
      Exploits the correlation between vertically neighboring samples in a field when interpolating intermediate pixels
      They have all pass temporal frequency response which guarantees the absence of motion artifacts
      Low computational cost
      Loss of vertical details and artifacts when the object exists in only one parity field
    • Intra field de-interlacing – Scan line duplication (Bob)
      The missing line is generated by duplicating the line directly above it
      Cons-
      Jagged effect will occur in the oblique line
      flicker
      Blur
    • Intra field de-interlacing – Scan line duplication (Bob)
      Jagged Effect
      Flicker
      Flicker
    • Intra field de-interlacing – Scan line interpolation
      The missing line is generated by taking the average of the lines vertically above and below it
      Less blur, flicker and jagging than scan line duplication method
    • Intra field de-interlacing – Edge Line Average (ELA)
      Interpolation is done in the direction of edge
      Three pixels in the previous scan line and the next scan line are referenced to determine the obvious edge in the image
      This method can eliminate the blurring effect of the bilinear interpolation and gives sharp/straight edges.
    • Intra field de-interlacing – Edge Line Average (ELA)
    • Inter field de-interlacing - Overview
      Based on Temporal filtering
      Exploits the correlation between temporally neighboring samples between fields when interpolating intermediate pixels
      They have all pass spatial frequency response which guarantees the absence of artifacts for stationary pictures
      Low computational cost but require additional field memory
      Artifacts in areas where motion occurs
    • Inter field de-interlacing – Field Insertion (Weave)
      • The two fields are “woven” into one frame
      • It is best solution in case of still images as all vertical frequencies are preserved
      • If there is motion between the even and odd fields being woven; artifact called serration or mouse teeth occurs because the moving objects are not shown at the same position for even and odd lines of the single output frame
      Odd Field
      Even Field
    • Inter field de-interlacing – Field Insertion (Weave)
      Difference between Bob and Weave
      Bob
      Weave
    • Inter field de-interlacing – Field Insertion (Weave)
      Mouse Teeth Artifact
    • Inter field de-interlacing – Temporal Averaging
      Types -
      Mean –
      Display a half-picture that is created as the average of the two original half-pictures
      The Mean algorithm simply pairs the original lines, and averages each pair into one output line. Line 1 of output is the mean of lines 1 and 2 in input, line 2 of output is the mean of lines 3 and 4 in input, and so on.
      Blend –
      Each line of the picture is created as the average of a line from the odd and a line from the even half-pictures.
      This ignores the fact that they are supposed to be displayed at different times
      The first line of output is copied from the first line of input. For any other output line N, the line is the mean of input lines N and N-1. That is, the second line of output is the mean of lines 1 and 2 in input, the third line is the mean of lines 2 and 3, ... and finally, the last line of output is the mean of the last two lines in input. The sliding averaging procedure preserves the original vertical resolution.
      Pros – Mouse Teeth artifact is avoided
      Cons – Artifact called ghosting is introduced
    • Inter field de-interlacing – Temporal Averaging
      Ghosting Artifact
    • Spatio-Temporal Interpolation - Overview
      Based on Spatio-Temporal filtering
      Exploits the correlation between both spatially and temporally neighboring samples when interpolating intermediate pixels
      The filter is usually designed such that the contribution from the neighboring fields is limited to the higher vertical frequencies
      Hence, motion artifacts are absent for objects without vertical details that move horizontally
      The vertical detail from the previous field is being combined with the temporally shifted current field, so some motion artifacts may occur.
    • Spatio-Temporal Interpolation
    • Motion adaptive de-interlacing - Overview
      It combines the advantages of both intra-field and inter-field interlacing
      It detects the motion first, and then adopts intra field de-interlacing in motion areas and inter-field de-interlacing in static areas
      High resolution and flicker free picture can be realized in motion and static area
      It relies on accurate motion detection; erroneous detection cause artifacts
    • Motion adaptive de-interlacing
    • Motion adaptive de-interlacing
      The motion detection block detects the motion between the two fields
      The output of the motion detection block controls the selector switch for the intra or inter interpolated values
      For moving image parts, intra interpolated values are passed and for static image parts inter interpolated values are passed
      Finally an interleaving switch interleaves the current field and the interpolated sample to form the progressive frame
      The intra and inter interpolation mechanism could be any of the techniques described earlier
    • Motion adaptive de-interlacing
      Instead of a switch, a fade mechanism could be used which combines the intra and inter interpolated samples
      • Fst is the result of interpolation for static image parts
      • Fmot is the result of interpolation for moving image parts
      • Motion detector determines the mix factor “alpha” with “alpha” = 0 for very high motion and “alpha” = 1 in absence of motion
    • Motion adaptive de-interlacing – Motion Detection
      Same-parity-field detection
      For interlaced video, the vertical position of even field and odd field is slightly different
      Same-parity-field detection detects the motion area by the even-field to even-field or odd-field to odd-field difference
    • Motion adaptive de-interlacing – Motion Detection
    • Motion adaptive de-interlacing – Motion Detection
      4-Field motion detection
      To improve the accuracy of motion detection, 4 fields rather than 2 fields are used
      The fields are labeled backward, current, forward and forward-forward
      The differences are computed for fields of same parity; backward-forward and current-forward-forward
    • Motion adaptive de-interlacing – Motion Detection
    • Motion adaptive de-interlacing – Motion Detection
    • Motion adaptive de-interlacing – Implicit Adaptation (Median Filtering)
      Median Filtering adapts implicitly to motion
      The simplest version is a 3-tap spatio-temporal filter which uses the immediate vertical up, down and temporal neighbor in the previous field
      The underlying assumption is that in case of stationarity, the temporal neighbor is likely to have a value between that of the vertical neighbors of the current field
      In case of motion, intra-field interpolation is likely to result, since the correlation between the samples in the current field is likely to be highest
      Hence Median filtering automatically realizes the intra/inter switch
      Pros: Superior properties at vertical edges and low hardware cost
      Cons: Distortion of vertical details and introduction of alias
    • Motion adaptive de-interlacing – Implicit Adaptation (Median Filtering)
    • Motion compensated de-interlacing - Overview
      It’s the most advanced technique for interpolation
      Motion compensated method try to interpolate in the direction with highest correlation; with motion vector available, this is the interpolation along the motion trajectory
      It allows the moving sequences to be converted virtually into stationary ones and use methods which work well on stationary sequences like line insertion etc.
      Motion compensated methods needs to be combined with scene change detection, otherwise it will attempt to find motion between two completely different scenes
      Usually a combination of Edge directed spatial interpolation and motion compensated temporal interpolation is used in practice
      It outperforms other techniques in terms of quality of the de-interlaced frame
      It’s the most complex and processing intensive technique as it involves motion estimation
    • Motion compensated de-interlacing
    • Motion compensated de-interlacing
      Motion estimation is performed on the fields of same parity (backward and forward fields) as they have the same sampling grid
      Half pel/quarter pel accuracy can be used for motion estimation
      As the backward and forward fields are of different parity than the current field, the blocks need to be shifted to the sampling grid of the current field
      To obtain the position of the block in the current interpolated field from the previous field, the motion vectors are halved and the blocks of backward, current and forward field moved simultaneously to the sampling grid of the current interpolated field
    • Motion compensated de-interlacing
      Block shifting to match the sampling grid
      Pixel value in the current interpolated field
    • Motion compensated de-interlacing
      To handle scene change detection, the pixel values obtained by motion compensated interpolation are compared with the spatial edge directed interpolated pixel values
      If the difference is beyond the threshold value, its assumed that interpolation based on motion estimation is incorrect
      In such case, directional interpolation is used instead of motion compensated interpolation
      The motion compensated/edge directed interpolated pixels are finally combined to get the progressive frame
    • References
      Altera White Paper - High-Definition Video Deinterlacing Using FPGAs
      Deinterlacing - An Overview
      GERARD DE HAAN, ERWIN B. BELLERS
      Deinterlacing using directional interpolation and motion compensation
      Ohjae Kwon, KwanghoonSohn, Chulhee Lee
      Motion Adaptive Interpolation with Horizontal Motion Detection for Deinterlacing
      Shyh-Feng Lin, Yu-Ling Chang, and Liang-Gee Chen
    • Thank You