**5.3.2 GM/LM based error concealment**

Obviously it is more effective to recover MVs of the EMBs in the global motion regions by the global MVs and the EMBs in local motion regions by the local motion compensation. And for the corrupted MBs located in the GM/LM overlapped regions, more accurate boundaries need to be searched using the advanced boundary matching criteria [19]-[21]. We give the detailed steps for the GM/LM based error concealment approach [22]. The detail diagram of the proposed GM/LM based EC method is shown in Fig.3.

GM/LM based EC method consists of the following four steps: 1) Carry out global motion estimation for the corrupted frames using the MVs of the correctly received MBs (CMBs). 2) Classify the CMBs into global motion MBs (GMBs) or local motion MBs (LMBs) types. 3) Determine the type of the erroneous MBs (EMBs) and Step 4. Carry out recovery by using the GM/LM based approach.

Based on the estimated global motion parameters, a CMB is classified into two types: GMB and LMB adaptively with respect to the matching error of the reconstructed MB (from the video streams) and the global motion warped MB. If the matching error is large enough then it is a LMB, otherwise a GMB. Actually this step does not influence the GM/LM based error concealment performances very much [22].

Fig. 3. Diagram of GM/LM based error concealment.

In GM/LM based EC approach, each EMB can be classified into one of the three types: GMB, LMB and GLMB according to the CMBs (including already recovered EMBs) type information in its 8-neighbors as follows:


A global motion based error concealment method is proposed by Su *et al.*[3,4]. In [3] MVs generated by global motion parameters are utilized to recover the EMBs under the assumption that they are all located in global motion regions. When the EMBs are in LM or GM/LM overlapped regions, usually the MVs generated by global motion parameters are

Obviously it is more effective to recover MVs of the EMBs in the global motion regions by the global MVs and the EMBs in local motion regions by the local motion compensation. And for the corrupted MBs located in the GM/LM overlapped regions, more accurate boundaries need to be searched using the advanced boundary matching criteria [19]-[21]. We give the detailed steps for the GM/LM based error concealment approach [22]. The

GM/LM based EC method consists of the following four steps: 1) Carry out global motion estimation for the corrupted frames using the MVs of the correctly received MBs (CMBs). 2) Classify the CMBs into global motion MBs (GMBs) or local motion MBs (LMBs) types. 3) Determine the type of the erroneous MBs (EMBs) and Step 4. Carry out recovery by using

Based on the estimated global motion parameters, a CMB is classified into two types: GMB and LMB adaptively with respect to the matching error of the reconstructed MB (from the video streams) and the global motion warped MB. If the matching error is large enough then it is a LMB, otherwise a GMB. Actually this step does not influence the GM/LM based error

In GM/LM based EC approach, each EMB can be classified into one of the three types: GMB, LMB and GLMB according to the CMBs (including already recovered EMBs) type

1. If the CMBs in the neighbors of an EMB are all with the type GMB, then we classify the EMB be a GMB. The corrupted pixels in the EMB are replaced by the warped pixels in

2. If the CMBs in the neighbors of an EMB are all with the type LMB, then we classify the EMB be a LMB. The corrupted pixels in the EMB can be replaced by the MB in their

detail diagram of the proposed GM/LM based EC method is shown in Fig.3.

incorrect to recover the lost MVs.

the GM/LM based approach.

concealment performances very much [22].

Fig. 3. Diagram of GM/LM based error concealment.

their reference frame by utilizing the GMV information.

information in its 8-neighbors as follows:

**5.3.2 GM/LM based error concealment** 

reference frame using the average MV of the non-corrupted or recovered MBs in its 8 neighbors. The GMV and LMV based replacement for the EMB are based on the facts that both global motion and local motion have certain consistence.

3. Otherwise the EMB is a GLMB. The EMB may contain both global and local motion regions. Boundaries between background and objects usually exist in the EMB. To determine the accurate boundaries, complicate boundary matching algorithms such as RBM, and AECOD [21] can be adopted. We use RBM method to search the optimal MV to recover the EMB.

#### **5.3.3 Error concealment performance**

Objective error concealment performances of the TR, TA, GM, RBM and GM/LM are given. Fig. 4 (a) and (b) show the objective averaged PSNR (peak signal to noise ratio) values of the EC methods applied to each of the P-frame of the testing sequences *flower* and *mobile* under the PER (packet error rates) 15%. From Fig.4, we find that our GM/LM based EC method gives comparatively better recovery results.

Fig. 4. EC Performances Comparison of the corresponding TR, TA, GM, RBM and GM/LM for the video sequences under PER 15%.

To show the subjective recovery results of the TR, TA, GM, RBM and GM/LM based error concealment approaches, two frames are extracted from the test video sequences with several erroneous slices, as shown in Fig. 5. We find that the recovery results of TR are not so effective. TA is not effective to get accurate motion information for the MBs in heavy motion regions. RBM performs well for the area where non-periodical texture appears. However, it is not so effective in the circumstance that the reference blocks are in smooth and texture similar regions as shown in Fig. 5(b). GM provides better recovery results for the background regions. However, large distortions are produced for recovering the EMBs in local motion regions. Comparatively, better performances are achieved by the proposed GM/LM based EC method.

Global Motion Estimation and Its Applications 95

Fig. 5. Subjective error concealment results for #31 of flower and #28 of foreman. (a) nonerror frames; (b) corrupted frames; and the corresponding recovery results of (c) TR;

The corresponding block diagram of the proposed GM/LM based text occluded region

Fig. 6. Block diagram of the GM/LM based text occluded region recovery (TORR). The input video is with text occluded regions and the output video is with text occluded region

Fig. 7. Diagram of recovering a pixel in text occluded region of current frame *j* from its previous frame *i* and next frame *k*. The dash lines means the pixel cannot be recovered from its reference frames. The solid lines means the pixels can be recovered from its reference

recovery (TORR) approach is shown in Fig.6. It consists of the following steps.

(d)TA;(e) GM; (f)RBM and (g) GM/LM respectively.

**5.4 GM/LM based text occluded region recovery** 

recovery.

frames.

f)

g)

a)

b)

c)

d)

e)

Fig. 5. Subjective error concealment results for #31 of flower and #28 of foreman. (a) nonerror frames; (b) corrupted frames; and the corresponding recovery results of (c) TR; (d)TA;(e) GM; (f)RBM and (g) GM/LM respectively.
