This paper presents an overview of the technologies for in-loop processing and filtering in the Versatile Video Coding (VVC) standard. These processes comprise luma mapping with chroma scaling, deblocking filter, sample adaptive offset, adaptive loop filter and cross-component adaptive loop filter. They are qualified as "in-loop" because they are applied inside the encoding and decoding loops, before storing the pictures in the decoded picture buffer. The filters are complementary and address different purposes. Luma mapping with chroma scaling aims at adaptively modifying the coded samples distribution for improved coding efficiency. The deblocking filter aims at reducing blocking discontinuities. Sample adaptive offset mostly aims at reducing artifacts resulting from the quantization of transform coefficients. Adaptive loop filter and cross-component adaptive loop filter are adaptive filters enabling to enhance the reconstructed signal, using for instance Wiener-filter encoding approaches. The paper provides an overview of the in-loop filtering process and a detailed description of the filtering algorithms. Objective compression efficiency results are provided for each filter, with indication of cumulative coding gains. Subjective benefits are illustrated. Implementation issues considered during the design of the VVC in-loop filters are also discussed.
The goal of multiple object tracking is to find the trajectory of the target objects through a number of frames from an image sequence. Generally, multi-object tracking is a challenging problem due to illumination variation, object occlusion, abrupt object motion and camera motion. In this paper, we propose a multi-object tracking scheme based on a new weighted Kanade-Lucas-Tomasi (KLT) tracker. The original KLT tracking algorithm tracks global feature points instead of a target object, and the features can hardly be tracked through a long sequence because some features may easily get lost after multiple frames. Our tracking method consists of three steps: the first step is to detect moving objects; the second step is to track the features within the moving object mask, where we use a consistency weighted function; and the last step is to identify the trajectory of the object. With an appropriately chosen weighting function, we are able to identify the trajectories of moving objects with high accuracy. In addition, our scheme is able to handle partial object occlusion.
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