Randa Atta

Low‐light image enhancement algorithms have been introduced to improve the visual quality of low‐light images that may degrade the performance of many computer vision and multimedia systems designed for high‐quality images. However, the existing bright channel prior and maximum colour channel enhancement algorithms introduce halo artifacts and colour distortions while enhancing the images. To overcome these limitations, in this paper, an effective fusion‐based low‐light image enhancement algorithm is proposed. In the proposed algorithm, the illumination of the low‐light image is estimated from both the bright and maximum colour channels to overcome the halo artifacts and colour distortion problems. Further, an effective refinement method is utilized to improve the sharpness of the initial enhanced image representing the scene reflectance. Experiment results show that the proposed algorithm outperforms the state‐of‐the‐art algorithms qualitatively and quantitatively. Moreover, the proposed algorithm reduces the halo artifacts and colour distortion and enhances the details while preserving the naturalness.

Rate control in scalable video coding (SVC) is a very challenging problem because of the inter-layer prediction structure which makes developing an efficient rate-control algorithm complex and difficult. Little prior work is available for joint temporal-quality (T-Q) scalability considering the rate-distortion (R-D) dependency among the temporal and quality layers. However, most of the rate control algorithms in SVC suffer from high computational complexity, growing significantly with the number of layers. In this paper, a single-pass joint temporal-quality rate-control algorithm is presented for H.264/SVC. In this algorithm, by analyzing the R-D dependency of joint T-Q scalability, Cauchy distribution-based rate-quantization (R–Q), and distortion-quantization (D–Q) models, a set of empirical values are first derived to estimate the initial values of the R-D model parameters for the joint temporal and quality layers. Then, a novel prediction mechanism to update these model parameters is proposed to allocate the bit budgets efficiently among the temporal and quality layers and hence to improve the performance of the proposed algorithm. Experimental results show that the proposed algorithm achieves better coding efficiency with low computational complexity compared to two other benchmark rate-control algorithms.