HEVC-Based Perceptually Adaptive Video Coding Using a DCT-Based Local Distortion Detection Probability Model

Discrete Cosine Transform (DCT)-based just noticeable difference (JND) profiles have widely been applied into human perception-based video coding in order to reduce perceptual redundancy, which is one of the main goals of perceptual video coding (PVC). However, there are two problems for this approach: 1) the JND value of each transform coefficient is estimated for a fixed-sized DCT kernel (e.g., 8 × 8), but flexible coding structures with variable-sized transform units have been utilized in standard video coding frameworks, such high efficiency video coding (HEVC) and 2) the DCT transform coefficients are suppressed by the amounts of JND values for the removal of perceptual redundancy, but the DCT transform coefficients of residues are not sufficiently suppressed due to many small transform coefficient values in mid- and high-frequency regions below the JND values. In order to solve these problems, we propose a more generalized visibility model in the DCT domain, called the DCT-based local distortion detection probability (LDDP) model that can estimate a degree of distortion visibility for any distribution of the transform coefficients of any sized DCT kernel for residues. Furthermore, we propose an HEVC-compliant LDDP-based PVC scheme where transform coefficients are sufficiently suppressed based on the LDDP model. The proposed PVC scheme is implemented in the HEVC Test Model (HM 11.0) reference software to show the effectiveness of the LDDP-based PVC scheme. Objective and subjective tests for encoded test sequences are performed. The experimental results show that the LDDP-based PVC scheme achieves a significant performance improvement of bitrate reduction at the similar visual quality levels compared with the original HM 11.0.