The Rate-Distortion adaptive mechanisms of MPEG-HEVC (High Efficiency Video Coding) and its derivatives are an incremental improvement in the software reference encoder, providing a selective Lagrangian parameter choice which varies by encoding mode (intra or inter) and picture reference level. Since this weighting factor (and the balanced cost functions it impacts) are crucial to the RD optimization process, affecting several encoder decisions and both coding efficiency and quality of the encoded stream, we investigate an improvement by modern reinforcement learning methods. We develop a neural-based agent that learns a real-valued control policy to maximize rate savings by input signal pattern, mapping pixel intensity values from the picture at the coding tree unit level, to the appropriate weighting-parameter. Our testing on reference software yields improvements for coding efficiency performance across different video sequences, in multiple classes of video.
In recent years, smartphones have become the primary device for day-to-day photography. Therefore, it is critical for mobile imaging to capture sharp images automatically without human intervention. In this paper, we formulate autofocus as a decisionmaking process, in which the travel distance of a lens is determined from the phase data obtained from the phase sensors of a smartphone, and the decision-making policy is based on reinforcement learning, a popular technique in the field of deep learning. We propose to use a noise-tolerant reward function to combat the noise of the phase data. In addition, instead of using only the current phase data, each lens movement is determined using the phase data acquired along the journey of an autofocus process. As a result, the proposed machine-learning approach is able to expedite the autofocus process as well. Experimental results show that the method indeed improves the autofocus speed.