Apple patents AI method to sharpen video quality during playback
Apple describes a system that uses a neural network to fix small errors created when video data is compressed. Instead of using standard math to undo the compression, the AI predicts the original details to make the picture clearer.
In the filing’s own words
Embodiments relate to a neural network coupled to a quantization circuitry, a transform circuitry, and a prediction circuitry. The neural network can generate a recovered transform block including recovered transform coefficients for a quantized transformed residual block generated by the quantization circuitry to reduce errors. A first error between the recovered transform block and a transformed residual block generated by the transform circuitry can be smaller than a second error between the transformed residual block and a dequantized transformed residual block generated by performing inverse quantization operations on the quantized transformed residual block, where the first error and the second error are determined based on a cost function.
What exactly they claim (claim 1)
1. A device, comprising: a network interface to receive a bit stream from an encoder, wherein the bit stream comprises a quantized transformed residual block of an input block of a plurality of input blocks of an image, wherein the quantized transformed residual block is generated by performing quantization operations by a quantization circuitry of the encoder on a transformed residual block for a residual block determined as a difference between the input block and a predicted block of the input block; a controller coupled to the network interface and to determine one or more decoding parameters for decoding the bit stream; and reconstruction circuitry comprising a neural network to generate a recovered transform block comprising recovered transform coefficients for the quantized transformed residual block to reduce errors introduced by the quantization circuitry, wherein a first error between the recovered transform block and the transformed residual block is smaller than a second error between the transformed residual block and a dequantized transformed residual block generated by performing inverse quantization operations on the quantized transformed residual block, and wherein the first error and the second error are to be determined based on a cost function.
Facts
- Company
- Apple (Apple Inc.)
- Official title
- NEURAL NETWORKS FOR TRANSFORM COEFFICIENT RECOVERY IN VIDEO CODEC
- Publication number
- US20260260387A1
- Application number
- 19443823
- Filed
- Jan 8, 2026
- Published
- Sep 3, 2026
- Inventors
- 9
- Claims
- 20
Classified under
G06T 9/002 Physics & computingH04N 19/124 Electricity & electronicsMore from Apple in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.