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Figure 4: Simulation Results for “Table Tennis” Sequence

Figure 4 Simulation Results for “Table Tennis” Sequence

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Rate-distortion function R(D) is the least amount of average mutual information which is needed to transmit some information within distortion D between the information transmitter and the receiver, which correspond to raw image and reconstructed image in image coding. Discrete stationary Gaussian process {a(n)} has been used to model the rate-distortion relation with the mean s uare distortion criterion in many references. In [7], when the discrete stationary Gaussian process {z(n),n = 0,4 the rate-distortion relation can be represented by equa t1,..., } has the spectral density function like equation (1), tion (2),(3) using the parameter 6. @, is the k-th element of the source correlation matrix and has a range of 0 < 6 < sup, ®(w). R(D) curve is generated when the parameter @ varies on the range. Rate-distortion relation of the image source can be predicted using the discrete stationary Gaussian process. If we assume that image source can be modeled by the two dimensional signal which is separated into horizontal and vertical direction, characteristics of one dimensional
Rate-distortion function R(D) is the least amount of average mutual information which is needed to transmit some information within distortion D between the information transmitter and the receiver, which correspond to raw image and reconstructed image in image coding. Discrete stationary Gaussian process {a(n)} has been used to model the rate-distortion relation with the mean s uare distortion criterion in many references. In [7], when the discrete stationary Gaussian process {z(n),n = 0,4 the rate-distortion relation can be represented by equa t1,..., } has the spectral density function like equation (1), tion (2),(3) using the parameter 6. @, is the k-th element of the source correlation matrix and has a range of 0 < 6 < sup, ®(w). R(D) curve is generated when the parameter @ varies on the range. Rate-distortion relation of the image source can be predicted using the discrete stationary Gaussian process. If we assume that image source can be modeled by the two dimensional signal which is separated into horizontal and vertical direction, characteristics of one dimensional
Figure 1: Observed Rate-Distortion Relation for the “Table Tennis” Sequence
Figure 1: Observed Rate-Distortion Relation for the “Table Tennis” Sequence
Figure 2: Target Bit Adjustment after Scene Change
Figure 2: Target Bit Adjustment after Scene Change
As measures of the performance, we select the average PSNR over 1 GOP and the variance of PSNR over £ GOP after scene changes to view the both effects of additional bit allocation and the suitable complexity updates The reason of choosing the variance of PSNR over 2 GOP is due to the fact that complexity update of I-picture after scene change takes about 1 GOP effecting the pictures to be coded with incorrect target bits over 2 GOF after scene change. Figure 4~6 show the generated bits and PSNR of Table Tennis, less and more complexity sequences after scene change. When coded with TM5 method, the reconstructed picture quality shows abrup degradation and fluctuation. It takes at least on GOP interval to recover. Using the proposed rate control method we can see that additional bit allocations are done on the scene changed pictures resulting in a less degradatior and small fluctuation of reconstructed picture quality. Table 1 summarizes the simulation results. However, there are still remains the buffer overflow problem. Though there are buffer regulation methods adjusting the target with the buffer status[2,8] , as mentioned in the previous section, the buffer control should be considered witl the bit allocation and remains for further study. > Conclusion
As measures of the performance, we select the average PSNR over 1 GOP and the variance of PSNR over £ GOP after scene changes to view the both effects of additional bit allocation and the suitable complexity updates The reason of choosing the variance of PSNR over 2 GOP is due to the fact that complexity update of I-picture after scene change takes about 1 GOP effecting the pictures to be coded with incorrect target bits over 2 GOF after scene change. Figure 4~6 show the generated bits and PSNR of Table Tennis, less and more complexity sequences after scene change. When coded with TM5 method, the reconstructed picture quality shows abrup degradation and fluctuation. It takes at least on GOP interval to recover. Using the proposed rate control method we can see that additional bit allocations are done on the scene changed pictures resulting in a less degradatior and small fluctuation of reconstructed picture quality. Table 1 summarizes the simulation results. However, there are still remains the buffer overflow problem. Though there are buffer regulation methods adjusting the target with the buffer status[2,8] , as mentioned in the previous section, the buffer control should be considered witl the bit allocation and remains for further study. > Conclusion
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