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An effective way of improving decoding performance of an LDPC code is to extend the single-decoder decoding method to a parallel decoding method with multiple sub-decoders. To this end, this paper proposes a parallel decoding method for the LDPC codes constructed by m-sequence. In this method, the sub-decoders have two types. The first one contains only one decoding module using the original parity-check constraints to implement a belief propagation (BP) algorithm. The second one consists of a pre-decode module and a decoding module. The parity-check matrices for pre-decode modules are generated by the parity-check constraints of the sub-sequences sampled from an m-sequence. Then, the number of iterations of the BP process in each pre-decode module is set as half of the girth of the parity-check matrix, resulting in the elimination of the impact of short cycles. Using maximum a posterior (MAP), the least metric selector (LMS) finally picks out a codeword from the outputs of sub-decoders. Our simulation results show that the performance gain of the proposed parallel decoding method with five sub-decoders is about 0.4 dB, compared to the single-decoder decoding method at the bit error rate (BER) of 10−5 .

Lane detection is the key technology of the intelligent vehicle based on machine vision. In order to improve the detection of real-time, a lane detection and prediction algorithm based on Randomized Hough Transform is developed in this paper. The algorithm includes lane detection algorithm and prediction algorithm. First of all at identification stages, scan the pretreated image in order to search lanes candidate points, and combine with the lanes angle range of constraints, fit the candidate boundary points by Randomized Hough Transform for the improvement of real-time and robustness. The driveway line prediction algorithm is also proposed. With the dynamic searching window, weights of prediction are adaptive and they can make the line prediction more accurate. Test results show that algorithm has good real-time and robust performance.

The block-based DCT image compression methods usually result in discontinuities called blocking artifacts at the boundaries of blocks due to the coarse quantization. A measurement of blocking artifacts based on Human Vision System (HVS) is proposed. This method separates the blocking effects from the original edges in the image by an adaptive edge detection based on local activity and luminance masking. The blocking artifacts in the non-edge area and on the edge are calculated separately. The weighted sum is regarded as the evaluation result. Simulation results show that the proposed measurement is robust for different kind of images, and has the general performance of image quality evaluation metric.