Qualcomm Incorporated

Prasant Shekhar Singh

Pawan Kumar Baheti

Naveen Srinivasamurthy

Narendranath Malayath

Mahant Siddaramanna

Apoorva Nagarajan

Systems and techniques are described for data encoding using a machine learning approach to generate a distortion prediction {circumflex over (D)} and a predicted bit rate {circumflex over (R)}, and to use {circumflex over (D)} and {circumflex over (R)} to perform rate-distortion optimization (RDO). For example, a video encoder can generate the distortion prediction {circumflex over (D)} and the bit rate residual predictionbased on outputs of the one or more neural networks in response to the one or more neural networks receiving a residual portion of a block of a video frame as input. The video encoder can determine bit rate metadata predictionbased on metadata associated with a mode of compression, and determine {circumflex over (R)} to be the sum ofand. The video encoder can determine a rate-distortion cost prediction &#x134; as a function of {circumflex over (D)} and {circumflex over (R)}, and can determine a prediction mode for compressing the block based on &#x134;.

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Bengaluru, India

Prasant Shekhar Singh