Adaptive Predictor Structures for Lossless Compression of Videos

Ashutosh Singla*, Jaya Shukla*, Anil Kumar Tiwari+, Sunil Prasad Jaiswal*, Vinit Jakhetiya**

*LNMIIT,Jaipur, Rajasthan, India

{ashutosh33.08,welcomejaya, suniljaiswal7}@gmail.com

+IIT Rajasthan, Jodhpur, Rajasthan, India

akt@iitj.ac.in

**HKUST, Hongkong,

vjakhetiya@ust.hk

In this paper, we propose a prediction algorithm that uses adaptive predictor structures for lossless video coding. The proposed encoder finds cross-correlation coefficient (Rxy) between current frame ( i) and motion compensated previous frame ( i-1) and classifies the coefficient value into a small number of bins. For general videos, we propose four bins and associate different predictor structures with each of the bins. Similarly for medical videos, numbers of bins are three and each of these bins is associated with different predictor structures.

In case of general videos, obtain the value of Rxy. If Rxy lies in the range of [0.75, 1], we will use motion compensated frame and then proceed with the steps from 1 to 3 given below. However, if Rxy < 0.75, we propose to use step 4.

1. If Rxy �[0.98, 1], we find error frame as i(x, y) = i (x, y) - i-1 (x, y). By doing this, we expect that temporal correlation is removed up to a large extent. For removal of spatial correlation, we classify error samples of i as per Gradient Adjusted Predictor (GAP) conditions and estimate LS based predictor of order 7 for each of the classes.The predictor structure is shown in Fig. 1(a).

2. If Rxy �[0.84, 0.98), this is a case where temporal correlation is a little lower than that mention in step 1. In this case, we propose to have a predictor structure that uses a large number of pixels from previous frame (motion compensated) and a few from the current frame. This predictor structure is given in Fig. 1(b). We classify the pixels of i as per GAP conditions with modified threshold value and estimate LS based predictor of order 7 for each of the classes.

3. If Rxy �[0.75, 0.84), in this case temporal correlation has further gone down. Under these cases, we propose to use a predictor structure that takes only one pixel from previous frame and four pixels from the current frame. This predictor structure is given in Fig. 1(c). We classify the pixels of i as per GAP conditions with modified threshold value and estimate LS based predictor of order 5 for each of the classes.

4. For frames with poor temporal correlation i.e. Rxy < 0.75, we propose to use intra frame predictor structure as shown in Fig. 1(d). We classify the pixels of i as per GAP conditions and estimate LS base predictor with this structure for pixels belonging to each of the classes.

However, in case of medical videos, we obtained Rxy and classifies into three bins and use different predictor structures. 1. If Rxy �[0.95, 1], we follow step 1 given above. 2. If Rxy �[0.90, 0.95), we follow step 2 given above. 3. For value of Rxy < 0.90, we follow step 4 given above.

Main contribution of our paper is to use adaptive predictor structures as a function of an estimate of cross-correlation existing between current frame and motion compensated previous frame. Effectiveness of this method is validated by experimenting on general videos and medical video sequences. It is found that our method gives consistently better performance as compared to the use of fixed predictor structure (shown in Fig. 1(b)) at lower computational complexity.

(a) (b) (c) (d)

Figure 1. Predictor structures used for predicting the frames. DNN, DNNE, DNW, DN, DNE, DWW, and DW represents difference between the pixel in the current frame (CF) and in the motion compensated previous frame (PF) at their respective position.

DNN DNNE

DNW DN DNE

DWW DW X

CF

N’ W’ X’ E’ S’

PF NW X

CF

X’

PF NW N NE W X

CF

NW N NE W X

CF

2012 Data Compression Conference

1068-0314/12 $26.00 © 2012 IEEE

DOI 10.1109/DCC.2012.66

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