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    Area of Science:

    • Video compression algorithms
    • Digital signal processing
    • Computational efficiency in media encoding

    Background:

    • Transform and quantization are computationally intensive steps in video encoding.
    • Many transform coefficients are quantized to zero, representing wasted computational effort.
    • Early detection of all-zero blocks (AZBs) can optimize encoding speed.

    Purpose of the Study:

    • To improve the efficiency of all-zero block (AZB) detection in video encoding.
    • To incorporate soft-decision quantization properties into AZB detection.
    • To reduce computational load without significant loss in rate-distortion (RD) performance.

    Main Methods:

    • Categorizing AZBs into genuine (G-AZBs) and pseudo (P-AZBs).
    • Employing sum of absolute transformed difference for G-AZBs.
    • Utilizing rate-distortion models and adaptive coefficient searching for P-AZBs.

    Main Results:

    • Achieved up to 24.16% time savings in transform and quantization.
    • Minimal RD performance loss (<0.06%).
    • Average total encoder time saving of 5.18%, up to 9.12%.
    • Detection accuracy for larger transform unit (TU) sizes reached 95%.

    Conclusions:

    • The proposed method effectively distinguishes between G-AZBs and P-AZBs.
    • Integrating soft-decision quantization properties enhances AZB detection accuracy and efficiency.
    • Significant computational savings are realized with negligible impact on video quality.