Adaptive Post-Decoder Memory for Low-Power 360° Video

360° video processing is rapidly gaining popularity in applications such as entertainment, medical visualization, and immersive training. However, compared to conventional video, 360° video requires significantly higher resources, including bandwidth, power, and memory capacity, due to the large volume of panoramic data. Importantly, not all regions of a 360° frame carry equal visual importance. In this work, we propose an adaptive post-decoder memory architecture that dynamically optimizes video data based on the region of interest. The decoded frame is divided into three regions: Field-of-View (FoV), border, and background. While FoV data is preserved without modification, border and background regions are compressed using flexible bit truncation during memory read operations. This approach reduces redundant data storage and memory bandwidth requirements. Experimental results demonstrate approximately 50% frame size reduction while maintaining about 42 dB WS-PSNR, achieving nearly 19.5% read power savings compared to conventional 360° video processing systems.