This paper presents several optimization algorithms for a High Efficiency Video Coding (HEVC) encoder based on single instruction multiple data (SIMD) operations and data-level parallelism. Based on the analysis of the computational complexity of HEVC encoder, we found that interpolation filter, cost function, and transform take around 68% of the total computation, on average. In this paper, several software optimization techniques, including frame-level interpolation filter and SIMD implementation for those computationally intensive parts, are presented for a fast HEVC encoder. In addition, we propose a slice-level parallelization and its load-balancing algorithm on multi-core platforms from the estimated computational load of each slice during the encoding process. The encoding speed of the proposed parallelized HEVC encoder is accelerated by approximately ten times compared to the HEVC reference model (HM) software, with minimal loss of coding efficiency.
In this paper, we present a pixel-wise unified rate quantization (R-Q) model for a low-complexity rate control on configurable coding units of high efficiency video coding (HEVC). In the case of HEVC, which employs hierarchical coding block structure, multiple R-Q models can be employed for the various block sizes. However, we found that the ratios of distortions over bits for all the blocks are a nearly constant because of employment of the rate distortion optimization technique. Hence, one relationship model between rate and quantization can be derived from the characteristic of similar ratios of distortions over bits regardless of block sizes. Thus, we propose the pixel-wise unified R-Q model for HEVC rate control working on the multi-level for all block sizes. We employ a simple leaky bucket model for bit control. The rate control based on the proposed pixel-wise unified R-Q model is implemented on HEVC test model 6.1 (HM6.1). According to the evaluation for the proposed rate control, the average matching percentage to target bitrates is 99.47% and the average PSNR degradation is 0.76 dB. Based on the comparative study, we found that the proposed rate control shows low bit fluctuation and good RD performance, compared to R-lambda rate control for long sequences.Index Terms-HEVC, rate control, unified R-Q model, video coding.
This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.
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