Intra-frame prediction-based point cloud attribute compression method

An intra-frame prediction-based point cloud attribute compression method, wherein a new block structure-based intra-frame prediction scheme is provided for point cloud attribute information, four prediction modes are provided to reduce information redundancy among different coding blocks as much as possible to improve point cloud attribute compression performance;

• the method comprises the steps of:
• 1) color space transformation of point cloud attributes:
• reading in point cloud attribute information to be processed, and transforming a point cloud color space from an RGB space to a YUV space according to the visual characteristics of human eyes and the difficulty degree of compression processing;
• 2) dividing the point cloud by using a KD tree to obtain coding blocks, and numbering the coding blocks according to a breadth traversal sequence
• reading in geometric information of the point cloud, performing KD tree division on the point cloud according to the geometric information, selecting a coordinate axis with a largest distribution variance in the position coordinate of the point cloud as a division axis each time, selecting a point with the coordinate being a median value as a division point, and performing iterative division until reaching a set KD tree depth;
• a block obtained by a last layer from the KD tree division being a coding block of the point cloud, numbering the coding blocks according to a breadth traversal order, and the number being used as a sequence for later processing of the coding blocks;
• 3) performing intra-frame prediction on the attribute information of the point cloud coding block based on the numbering sequence, with four prediction modes:
• when the intra-frame prediction is performed on a first coding block b1, 128 is used as a reference value for Y-component prediction;
• when color components Yi, Ui, and Vi of other blocks bi (i≠1) are processed, 128 is used as a prediction reference value of Yi in a prediction mode I, and Ui and Vi are not predicted;
• a mean value Yi-1 of Y-components of all points after reconstruction of a previous block bi-1 is used for predicting Yi in the prediction mode II, and Ui and Vi are not predicted;
• mean values Ui-1 and Vi-1 of the U and V color components of all points after the reconstruction of the previous block are used for predicting Ui and Vi in the prediction mode III, and Yi is not predicted;
• Yi-1, Ui-1, and Vi-1 are used as reference values of Yi, Ui and Vi for predicting in the prediction mode IV;
• and the prediction modes comprises four prediction modes;
• 4) mode decision for intra-frame prediction:
• four modes are used for predicting color components Yi, Ui, and Vi of the coding block bi (i≠1), an optimal prediction mode is required to be selected by mode decision which is not required for a first coding block;
• estimating a cost of a prediction mode using a Sum of Absolute Transformed Differences (SATD) and an absolute value of a prediction residual transformation coefficient, wherein the prediction residual is transformed using a Discrete Cosine Transform (DCT);
• a smaller SATD value represents a lower cost of the prediction mode and better prediction performance, and the mode with the smallest SATD is selected as the prediction mode of the current block;
• and
• 5) generation of a code stream with point cloud attribute compression:
• processing all the coding blocks according to the coding sequence, performing DCT transformation, uniform quantization and entropy coding on the predicted residual to obtain a final code stream with point cloud attribute compression.