Hierarchical Feature Fusion and Adaptive Convolutional Embedding Network for Efficient Video Compression with Block-Based Prediction Mode Selection

High Efficiency Video Coding (HEVC) is successful at minimizing the video bitrate, but often creates easily visible artifacts like blocking, ringing, and loss of texture, particularly with low-bitrate conditions. This paper introduces the Hierarchical Feature Fusion and Adaptive Convolutional Embedding Network (H2F-ACEN), which is a two-step architecture aimed at the enhancement of HEVC-decoded frames with limited computational costs. In the first phase, a U-Shaped Encoder–Decoder (USED) block is used to optimize the Quantization Bit Stream (QBS) to silence redundant features and reinforce spatial encoding. The second is the Double-Decker CNN and Hierarchical Embedding Network (HEN), which uses the encoded frames to optimize held decoded frames by means of multi-scale representation learning and residual artifact removal. Quantum-inspired feature extraction layer is a layer that is implemented on classical hardware that provides amplitude-based feature diversification without hardware quantum computation. In order to achieve a better stability in the training process and lower the convergence stagnation, a Triangular Topology Aggregation Optimizer (TTAO) adapts the model parameters of the two stages. The experimental assessments of five benchmark sequences, Lady in Rain, Joy Scene, Rain Fall, Vegetables, and Flower, show that there are substantial objective and perceptual video quality improvements. The average PSNR gain of 2.5 dB and higher SSIM and a MOS score of 4.82 achieved by the proposed model outperform the current techniques like CBT-Net, MBC-LBC, and FVC. These findings make H2F-ACEN a powerful and scalable real-time HEVC video enhancement solution.