6.1 Conclusion
In this thesis, we implemented an I-Frame decoder with parallelism technique and memory reduction method to achieve low memory, high throughput HEVC video decoder.
From the system point of view, first considering the memory requirement in the total video decoder, the intra predictor and deblocking filter occupy almost 83% totally and the power con-sumption occupy about 15.3% of all. Therefore, with higher resolution, the memory require-ments and power consumption would harm the hardware processing. By the proposed shared above line buffer for the deleting 1-line buffer for deblocking filter, it could save the internal memory to the 20% reduction ratio of all. Further, the prediction-based memory hierarchy for containing 1/8 frame size of internal SRAM for the deblocking filter is limited for the system point considering the external memory bandwidth and SDRAM power consumption. Compared with the [27], the hit rate could achieve better performance in the SRAM reduction factor set as 8. In Figure 5.8, the hit rate is improved about 14% in the Full-HD sequence. While the external memory bandwidth in the miss penalty is reduced about the 36.1% in the Full-HD se-quence. For accessing the external memory, the power consumes in the SDRAM would also be considered to the system. In Table 5.3, the SDRAM power could also be reduced 36% for only 3.6mW. In the proposed architecture, the I-frame decoder consists of the transform coder,
intra predictor and in-loop filter which are improved the throughput which can be enhanced to decode 8Kx4K super-high vision by using the wavefront parallel processing and the internal memories could be reduced using the shared above line buffer and the second level memory hierarchy, as a result, the proposed work could be applied to the super-high vision mulimedia application and the low power portable devices.
6.2 Future Work
In the future work, the application of the multimedia video codec could be applied to the intelligent video surveillance (IVS). The engine camera could track and record the motion of the objects and also identify the information of the objects. Moreover, as the information of the video could appeal to the viewers, the decoder must combine with the detection engine used to specify the human’s gestures and also the cloud computing is the important technique to complete this application.
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