Block truncation coding (BTC) is an efficient image compression algorithm that generates a constant output bit-rate. For color image compression, vector quantization (VQ) is exploited to improve the coding efficiency. In this letter, we propose an improved VQ based BTC (VQ-BTC) algorithm using template matching and Lloyd quantization (LQ). The experimental results show that the proposed method improves the PSNR by 0.9 dB in average compared to the conventional VQ-BTC algorithms.

The quality of visual comfort and depth perception is a crucial requirement for virtual reality (VR) applications. This paper investigates major causes of visual discomfort and proposes a novel virtual camera controlling method using visual saliency to minimize visual discomfort. We extract the saliency of each scene and properly adjust the convergence plane to preserve realistic 3D effects. We also evaluate the effectiveness of our method on free-form architecture models. The results indicate that the proposed saliency-guided camera control is more comfortable than typical camera control and gives more realistic depth perception.