Making the best of legacy video on modern displays

Abstract

Abstract— The recent revolution in display technology enables bright high‐resolution displays, thereby removing one of the bottlenecks in video quality. How to profit from HD flat‐panel displays when watching legacy SD‐video material will be discussed. An overview of resolution up‐conversion techniques will be presented, including an objective (MSE‐scores) and a subjective assessment. Screenshots are also included to illustrate the quality of the algorithms.

Class-count reduction techniques for content adaptive filtering

“…Suppose X k denotes all the input vectors in content class k. We sort the input vectors X 1 , X 2 , ..., X k to X [1] , X [2] , ..., X [k] from high to low by the occurrence frequency of its content classes. The M least frequent content classes will be merged into a cluster.…”

“…3 shows a block diagram of using such comparators. M −1 comparators contain the class codes C [1] , C [2] , ..., C [M −1] which are sorted by its occurrence frequency. Once the input pixels are classified by different content classifications, the class code will be compared with the M −1 most frequent occurring class codes.…”

“…Content adaptive filtering has received considerable attention recently due to its superior performance over fixed filters [1]. A content adaptive filter first classifies local image content based on different image features, such as structures and contrast.…”

Simultaneous coding artifact reduction and sharpness enhancement for block-based compressed images and videos

“…Adaptive dynamic range coding (ADRC) is adopted for classification in [8,9]. Zhao et al [10] evaluated several approaches for upscaling and concluded that the classification-based least mean square (LMS) algorithm outperforms the others. Furthermore, the LMS optimization technique has also been applied on coding artifact reduction and sharpness enhancement [11,12] that yields promising results.…”

Coding Artifacts Robust Resolution Up-conversion

“…Advanced resolution up-conversion algorithms [1][2][3][4][5][6] are designed to be adaptive to local structure or edge orientation, which makes them capable of preserving edges and fine details in the image content. Zhao et al [7] compared the state-of-the-art up-scaling techniques both objectively and subjectively, and concluded that the structure-adaptive LMS training technique, proposed by Kondo et al [1], performs the best. The training algorithm can preserve structures and fine textures perfectly, when the image is clean and noise free.…”