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EE368B Project. Rate-Constrained Conditional Replenishment with Adaptive Change Detection. Xinqiao Liu December 8, 2000. Motivation. Conditional replenishment ---- method of reducing temporal redundancy between successive frames

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rate constrained conditional replenishment with adaptive change detection

EE368B Project

Rate-Constrained Conditional Replenishment with Adaptive Change Detection

Xinqiao Liu

December 8, 2000

Rate constrained conditional replenishment

motivation
Motivation
  • Conditional replenishment ---- method of reducing temporal redundancy between successive frames
    • Efficient in video conferencing with stationary cameras and slow motion.
    • Study shows that less than 3% of the pixels need to be replenished in most head-and-shoulders scenes in desktop video
  • Computational complexity is significant simpler than other video compression methods
    • Software-only CODEC is possible
    • Appealing for on-sensor compression where pixel array and simple image processing are integrated on the same chip, i.e, camera system-on-chip

Rate constrained conditional replenishment

previous work
Previous Work
  • Most of the research concentrate mainly on the image quality (Haskell, et al’72, Haskell’79)
  • Recently, a perception-based change detection method was proposed (Chiu&Berger ’96, Chiu&Berger’99)
    • Reduces the perceptual redundancy in addition to the spatial and temporal redundancy
    • Change detection threshold is set based on Web’s law
  • However, the correlation between transmission bit-rate and the choice of change detection schemes still need to be explored.

Rate constrained conditional replenishment

outline
Outline
  • Introduction & Problem formulation
  • Context-based Arithmetic Encoder
  • Change detection --- direct methods
    • Subsampling
    • Threshold adjusting
  • Adaptive change detection
    • Noise characteristic
    • Adaptive algorithm
  • Conclusion

Rate constrained conditional replenishment

conditional replenishment diagram
Conditional Replenishment Diagram

Goal: Given a rate-constrained transmission channel, find the optimal change detection algorithm that minimizes the distortion

Rate constrained conditional replenishment

model and assumptions
Model and Assumptions

Assumptions:

  • Transmitted separately under certain bit-rate constrain R1, R2
  • Lossless coding for both mask and signal
  • Only intra-frame compression is considered

Rate constrained conditional replenishment

rate constrained change detection
Rate-Constrained Change Detection
  • Three ways to control the bit rate in the change detector:
    • Subsampling the mask and signal after detection
    • Adjusting the detection threshold
    • Using adaptive threshold for each pixel based on the noise characteristics -----eliminate those pixels that have changed due to noise rather than the input
  • Use unconstrained Lagrangian cost function to find the optimum detection parameters for each method

Rate constrained conditional replenishment

problem formulation i
Problem Formulation (I)

Given previous frame A1, current frame A2, binary change mask C, the reconstructed frame at decoder end is:

The mean-square distortion is defined as:

Assume R1 = kR2 since they are proportional to the number of changed pixels. The total bit-rate R is

The above assumption allows us to study the rate-distortion function of conditional replenishment by only implementing the compression scheme of the mask.

Rate constrained conditional replenishment

problem formulation ii
Problem Formulation (II)

The constrained problem of:

Can be converted to the unstrained problem by introducing the Lagrangian cost function given Lagrange multiplier l:

where s is the adjustable change detection parameter. The optimal value of s is given by:

The desired optimal slop value l* is not known a priori but can be obtained using a fast bisection search algorithm

Rate constrained conditional replenishment

outline10
Outline
  • Introduction & problem formulation
  • Context-based Arithmetic Encoder
  • Change detection --- direct methods
  • Adaptive change detection
  • Conclusion

Rate constrained conditional replenishment

test video sequence
Test Video Sequence
  • Captured by a stationary high-speed digital camera with a person moving cross the screen:

Rate constrained conditional replenishment

context based arithmetic encoder cae
Context-based Arithmetic Encoder (CAE)
  • Binary bitmap-based shape coding scheme used in the MPEG-4 standard
  • Three types of 16x16 macroblocks:
    • "black" block: none of the pixel changed (all 0)
    • "white" block: all pixels changed and to be replenished (all 1)
    • “boundary” block: encoded with a template of 10 pixels to define the causal context for predicting the binary value of the current pixel (S0).

For black and white blocks, only the block type need to be transmitted

For boundary blocks, use conditional entropy:

Rate constrained conditional replenishment

outline13
Outline
  • Introduction & problem formulation
  • Context-based Arithmetic Encoder
  • Change detection --- direct methods
    • Subsampling
    • Threshold adjusting
  • Adaptive change detection
  • Conclusion

Rate constrained conditional replenishment

change masks with subsampling
Change Masks With Subsampling
  • Subsample the macroblock by a factor of 2, 4 or 8
  • Subblocks are encoded using the CAE
  • Upsample at the decoder end using pixel replication filter combined with a 3x3 median filter

Rate constrained conditional replenishment

rate distortion of subsampling
Rate-distortion of Subsampling

Rate constrained conditional replenishment

change masks with threshold adjusting
Change Masks With Threshold-adjusting
  • Control the bit-rate by globally adjusting the change detector threshold. As the threshold increased, few pixels will be detected

Rate constrained conditional replenishment

rate distortion of threshold adjusting
Rate-distortion of Threshold-adjusting

Rate constrained conditional replenishment

outline18
Outline
  • Introduction & problem formulation
  • Context-based Arithmetic Encoder
  • Change detection --- direct methods
  • Adaptive change detection
    • Noise characteristics
    • Adaptive algorithm
  • Conclusion

Rate constrained conditional replenishment

noise characteristics
Noise Characteristics
  • A fundamental problem in designing an optimum change detector is how to separate pixels whose change is due to noise from pixels whose change is due to real input signal change
  • For cameras using either CCD or CMOS image sensors, the final image is formed by the photo-charge Qi,j(or voltage) integrated on each photo-detector during the exposure time. Two independent additive noise corrupt the output signal:
    • Shot noise Ui,jwhich is zero mean signal dependent gaussian distribution with:
    • Readout circuit and reset noise Vi,j (including quantization noise) with zero mean and variance dV2.

Rate constrained conditional replenishment

adaptive change detection
Adaptive Change Detection
  • Thus the total noise variance of pixel (i,j) is:
    • The noise is signal dependent
    • The stronger the luminance level, the noisier the pixel will be
  • The threshold Ti,j is thus set as:
    • where m is the sensitivity factor that is set globally
    • is local average value over a small area with size 8x8.
  • Note that by changing m, we effectively adjusting the detection sensitivity while the threshold is still locally adapted

Rate constrained conditional replenishment

adaptive threshold
Adaptive Threshold

Rate constrained conditional replenishment

change masks with adaptive threshold
Change Masks With Adaptive Threshold

Rate constrained conditional replenishment

rate distortion of adaptive threshold
Rate-distortion of Adaptive Threshold

Rate constrained conditional replenishment

performance comparison
Performance Comparison

Subsampling is the most efficient in reducing bit-rate

Adaptive thresholding achieves the best PSNR

Rate constrained conditional replenishment

conclusion
Conclusion
  • Studied three change detection algorithms:
      • Subsampling
      • Threshold-adjusting
      • Adaptive threshold based on the noise characteristics
  • The adaptive change detection algorithm efficiently separates pixels whose change is due to noise from pixels whose value change is due to real input signal change
  • Simulation proves that the adaptive change detection algorithm achieves the best PSNR among all the three algorithms

Rate constrained conditional replenishment