In the intelligent parking space management system， a decrease in accuracy and effectiveness of parking space prediction can be caused by factors such as illumination changes and parking space occlusion. To overcome this problem， a parking space detection method based on self-supervised learning HOG （Histogram of Oriented Gradient） prediction auxiliary task was proposed. Firstly， a self-supervised learning auxiliary task to predict the HOG feature in occluded part of image was designed， the visual representation of the image was learned more fully and the feature extraction ability of the model was improved by using the MobileViTBlock （light-weight， general-purpose， and Mobile-friendly Vision Transformer Block） to synthesize the global information of the image. Then， an improvement was made to the SE （Squeeze-and-Excitation） attention mechanism， thereby enabling the model to achieve or even exceed the effect of the original SE attention mechanism at a lower computational cost. Finally， the feature extraction part trained by the auxiliary task was applied to the downstream classification task for parking space status prediction. Experiments were carried out on the mixed dataset of PKLot and CNRPark. The experimental results show that the proposed model has the accuracy reached 97.49% on the test set； compared to RepVGG， the accuracy of occlusion prediction improves by 5.46 percentage points， which represents a great improvement compared with other parking space detection algorithms.

In order to improve image representation efficiency, an improved rectangle Non-symmetry Anti-packing representation Model (NAM) named IRNAM was proposed for image coding. This scheme adopted double-rectangle sub-patterns to represent gray image, integrated bit plane optimization strategy and stored the sub-patterns continuously, thus the number of sub-patterns had decreased sharply. The experimental results show that compared with rectangle NAM algorithm and other improved NAM algorithms, the number of sub-patterns can be reduced apparently after employing IRNAM algorithm, which can save data storage space effectively and prove to be a high performance image representation method.

A high quality volume rendering technique was presented to render a large volume dataset at interactive rates on standard PC hardware. We employed 3D texture mapping as a core rendering engine and took advantage of combinations of HW-supported visibility tests such as occlusion queries, stencil tests and programmable shaders to accelerate the whole rendering process. We have implemented the volume rendering algorithm for large volume data, and achieved real-time performance without any loss of image quality.