Video Encoding Transformation Matrix Selection

The patent segments the transformation matrix selection process by dividing residual blocks into different categories based on their statistical features (e.g., variance, energy distribution). Instead of using a single transformation matrix for all residual blocks within a prediction mode, the patent selects different transformation matrices for different segments of residual blocks, thereby improving encoding efficiency while maintaining manageable complexity.

The patent introduces dynamic transformation matrix selection where the transformation matrix is not fixed for a given prediction mode but is dynamically chosen based on the actual statistical characteristics of each residual block. This dynamic adaptation allows the system to optimize encoding efficiency for each specific residual block while keeping the overall system complexity controlled through predefined matrix sets.

The patent applies preliminary action by pre-defining multiple candidate transformation matrices and organizing them into sets before the actual encoding process. During encoding, the system only needs to select from these pre-prepared matrices based on simple criteria (such as residual block variance thresholds), rather than computing optimal matrices in real-time. This significantly reduces computational complexity while maintaining the benefit of adaptive matrix selection.

The patent changes parameters by using simplified selection criteria (such as variance thresholds or energy metrics) to choose transformation matrices, rather than performing complex rate-distortion optimization for each block. This parameter-based selection approach maintains encoding efficiency improvements while reducing the computational burden and device complexity.

The patent applies local quality by assigning different transformation matrices to different local characteristics of residual data. Instead of using a uniform transformation matrix for all prediction modes and all residual blocks, the patent selects transformation matrices that are locally adapted to the specific statistical features of each residual block, thereby improving encoding efficiency without requiring a complete redesign of the transformation system.

The patent achieves universality by creating a set of transformation matrices that can serve multiple prediction modes and multiple types of residual blocks. Rather than having dedicated transformation matrices for each prediction mode (which would increase complexity), the patent designs a universal set of matrices that can be selectively applied based on residual block characteristics, maintaining both simplicity and adaptability.